diff --git a/README.md b/README.md index a6976012..e22436da 100644 --- a/README.md +++ b/README.md @@ -158,13 +158,15 @@ The spatial stage creates the transaction network topology. This determines whic #### 1. Scale-Free Network Blueprint -AMLGentex generates scale-free networks where node degree follows a truncated discrete power-law distribution: +AMLGentex generates scale-free networks where node degree follows a truncated discrete power-law distribution with exponential cutoff: -$$P(K=k) \propto k^{-\gamma}, \quad k \in \{k_{\min}, \ldots, k_{\max}\}$$ +$$P(K=k) \propto k^{-\gamma} \exp\left(-\frac{2k}{k_{\max}}\right), \quad k \in \{k_{\min}, \ldots, k_{\max}\}$$ + +The exponential cutoff provides softer tail truncation, more realistic for finite-size networks and preventing extreme degree concentration near k_max. **Parameters:** - **`kmin`**: Minimum degree (default: 1) -- **`kmax`**: Maximum degree (default: n-1, capped for simple graphs) +- **`kmax`**: Maximum degree (default: floor(√n), capped at n-1 for simple graphs) - **`gamma`**: Power-law exponent (optional - solved from average_degree if not provided) - **`average_degree`**: Target mean degree (specify this OR gamma) @@ -172,11 +174,44 @@ $$P(K=k) \propto k^{-\gamma}, \quad k \in \{k_{\min}, \ldots, k_{\max}\}$$ The expected degree for a given γ is: -$$\mu(\gamma) = \frac{\sum_{k=k_{\min}}^{k_{\max}} k^{1-\gamma}}{\sum_{k=k_{\min}}^{k_{\max}} k^{-\gamma}}$$ +$$\mu(\gamma) = \frac{\sum_{k=k_{\min}}^{k_{\max}} k \cdot k^{-\gamma} \exp(-2k/k_{\max})}{\sum_{k=k_{\min}}^{k_{\max}} k^{-\gamma} \exp(-2k/k_{\max})}$$ This function is strictly decreasing in γ: smaller γ → heavier tail → larger mean; larger γ → mass concentrates at k_min → smaller mean. -Given a target average degree, we solve μ(γ) = target using Brent's method. Truncation (finite k_max) guarantees a finite mean for any γ > 0, and monotonicity ensures a unique solution. +Given a target average degree, we solve μ(γ) = target using Brent's method. The exponential cutoff ensures smooth decay near k_max, and monotonicity guarantees a unique solution. + +**Example: n=10,000 nodes (kmax=100)** + +| Target Mean | γ | +|-------------|-------| +| 1.5 | 2.67 | +| 2.0 | 2.23 | +| 3.0 | 1.85 | +| 5.0 | 1.49 | +| 10.0 | 1.06 | +| 20.0 | 0.58 | + +**Survival function P(K > k):** + +| k | μ=1.5 | μ=2.0 | μ=3.0 | μ=5.0 | μ=10.0 | μ=20.0 | +|-----|-------|-------|-------|-------|--------|--------| +| 1 | 21.1% | 30.3% | 41.4% | 54.5% | 72.2% | 88.7% | +| 5 | 2.2% | 5.2% | 11.0% | 20.9% | 40.2% | 66.7% | +| 10 | 0.6% | 1.9% | 5.0% | 11.3% | 26.3% | 51.7% | +| 20 | 0.1% | 0.6% | 1.9% | 5.1% | 14.4% | 34.1% | +| 50 | <0.1% | 0.1% | 0.3% | 1.0% | 3.7% | 11.3% | +| 90 | <0.1% | <0.1% | <0.1% | 0.1% | 0.3% | 1.2% | + +**Expected nodes with degree > k (n=10,000):** + +| k | μ=1.5 | μ=2.0 | μ=3.0 | μ=5.0 | μ=10.0 | μ=20.0 | +|-----|-------|-------|-------|-------|--------|--------| +| 10 | 62 | 192 | 498 | 1,133 | 2,633 | 5,175 | +| 20 | 14 | 58 | 186 | 509 | 1,441 | 3,415 | +| 50 | 1 | 7 | 29 | 99 | 365 | 1,128 | +| 90 | <1 | <1 | 2 | 8 | 33 | 121 | + +The exponential cutoff smoothly suppresses extreme degrees rather than imposing a hard wall at kmax. #### 2. Pattern Injection diff --git a/experiments/template_experiment/config/data.yaml b/experiments/template_experiment/config/data.yaml index ad9a71f0..4ccef4f9 100644 --- a/experiments/template_experiment/config/data.yaml +++ b/experiments/template_experiment/config/data.yaml @@ -75,13 +75,15 @@ optimisation_bounds: # ML Selector parameters (controls biased account selection for AML patterns) ml_selector: structure_weights: - degree: [0.1, 0.5] - betweenness: [0.1, 0.5] - pagerank: [0.1, 0.5] + degree: [-0.3, 0.3] + betweenness: [-0.3, 0.3] + pagerank: [-0.3, 0.3] kyc_weights: - init_balance: [0.0, 0.2] - salary: [0.0, 0.1] - age: [0.0, 0.1] + init_balance: [-0.2, 0.2] # Negative = prefer low-balance accounts + salary: [-0.1, 0.1] + age: [-0.1, 0.1] + propagation_weights: + city: [0.0, 0.5] # Controls geographic/network clustering of SAR accounts participation_decay: [0.1, 0.3] scale-free: # Truncated discrete power law parameters: P(K=k) ∝ k^(-gamma), k ∈ {kmin, ..., kmax} @@ -90,6 +92,10 @@ scale-free: # kmax: null # Maximum degree (defaults to n-1 if not specified) # gamma: 2.0 # Power law exponent (if not provided, solved from average_degree) average_degree: 2.0 # Target mean degree (used to solve for gamma if gamma not provided) +normal_patterns: + # Controls how main accounts are selected for normal transaction patterns. + # Participation decay prevents high-degree nodes from dominating pattern membership. + participation_decay: 0.5 # Factor applied after each selection (0.5 = halve weight each time) ml_selector: # Money Laundering Account Selector Configuration # Controls biased selection of accounts for AML typology injection diff --git a/src/data_creation/generator.py b/src/data_creation/generator.py index df1a2df4..0444e00c 100644 --- a/src/data_creation/generator.py +++ b/src/data_creation/generator.py @@ -100,7 +100,7 @@ def run_spatial(self, force=False): # Step 1: Generate degree distribution if needed (goes to spatial output) degree_path = spatial_output / degree_file - if not degree_path.exists(): + if force or not degree_path.exists(): logger.info(f" [1/2] Generating degree distribution...") start = time.time() # Call directly with config dict (has absolute paths) @@ -223,7 +223,7 @@ def run_spatial_baseline(self, force=False): # Step 1: Generate degree distribution if needed (goes to spatial output) degree_path = Path(output_dir) / degree_file - if not degree_path.exists(): + if force or not degree_path.exists(): logger.info(f" [1/2] Generating degree distribution...") start = time.time() generate_scalefree.generate_degree_file_from_config(self.config) diff --git a/src/data_creation/spatial_simulation/generate_scalefree.py b/src/data_creation/spatial_simulation/generate_scalefree.py index 7da2981a..4646b0a1 100644 --- a/src/data_creation/spatial_simulation/generate_scalefree.py +++ b/src/data_creation/spatial_simulation/generate_scalefree.py @@ -4,7 +4,12 @@ This module implements proper discrete power-law sampling for directed graphs, following the specification for truncated discrete power law on k = kmin..kmax. -P(K=k) ∝ k^(-gamma), k ∈ {kmin, ..., kmax} +Distribution forms: +- Pure power law: P(K=k) ∝ k^(-gamma) +- With exponential cutoff: P(K=k) ∝ k^(-gamma) * exp(-2k/kmax) + +The exponential cutoff provides a softer tail truncation, more realistic for +finite-size networks and preventing extreme degree concentration at kmax. """ import numpy as np @@ -22,7 +27,12 @@ def truncated_discrete_powerlaw_pmf(k_values: np.ndarray, gamma: float) -> np.ndarray: """ - Compute PMF for truncated discrete power law: P(K=k) ∝ k^(-gamma). + Compute PMF for truncated discrete power law with exponential cutoff. + + P(K=k) ∝ k^(-gamma) * exp(-2k/kmax) + + where kmax is inferred from max(k_values). The exponential cutoff provides + a softer tail truncation, more realistic for finite networks. Args: k_values: Array of integer degree values (support of distribution) @@ -34,13 +44,17 @@ def truncated_discrete_powerlaw_pmf(k_values: np.ndarray, gamma: float) -> np.nd if gamma <= 0.0: raise ValueError(f"gamma must be > 0.0, got {gamma}") - unnormalized = np.power(k_values.astype(float), -gamma) + kmax = k_values.max() + + # Power law with exponential cutoff + unnormalized = np.power(k_values.astype(float), -gamma) * np.exp(-2.0 * k_values / kmax) + return unnormalized / unnormalized.sum() def truncated_discrete_powerlaw_mean(kmin: int, kmax: int, gamma: float) -> float: """ - Compute mean of truncated discrete power law. + Compute mean of truncated discrete power law with exponential cutoff. Args: kmin: Minimum degree (inclusive) @@ -59,7 +73,7 @@ def solve_gamma_for_mean( kmin: int, kmax: int, target_mean: float, - gamma_bounds: Tuple[float, float] = (1.01, 20.0) + gamma_bounds: Tuple[float, float] = (0.01, 20.0) ) -> float: """ Solve for gamma such that truncated discrete power law has target mean. @@ -70,7 +84,7 @@ def solve_gamma_for_mean( kmin: Minimum degree kmax: Maximum degree target_mean: Desired mean degree - gamma_bounds: Search bounds for gamma (default: 1.01 to 20.0) + gamma_bounds: Search bounds for gamma (default: 0.01 to 20.0) Returns: gamma value that achieves target_mean @@ -110,7 +124,7 @@ def sample_truncated_discrete_powerlaw( rng: np.random.Generator ) -> np.ndarray: """ - Sample n values from truncated discrete power law. + Sample n values from truncated discrete power law with exponential cutoff. Args: n: Number of samples @@ -233,10 +247,12 @@ def discrete_powerlaw_degree_distribution( Samples in-degrees and out-degrees separately from the same distribution, then balances sums with integer-only adjustments. + Distribution: P(K=k) ∝ k^(-gamma) * exp(-2k/kmax) + Args: n: Number of nodes kmin: Minimum degree (default: 1) - kmax: Maximum degree (default: n-1) + kmax: Maximum degree (default: floor(sqrt(n))) gamma: Power law exponent. If None, solved from average_degree. average_degree: Target mean degree. Required if gamma is None. seed: Random seed @@ -256,7 +272,7 @@ def discrete_powerlaw_degree_distribution( if kmin < 0: raise ValueError(f"kmin must be >= 0, got {kmin}") if kmax is None: - kmax = n - 1 + kmax = min(int(np.floor(np.sqrt(n))), n - 1) if kmax < kmin: raise ValueError(f"kmax ({kmax}) must be >= kmin ({kmin})") if kmax > n - 1: @@ -379,7 +395,7 @@ def generate_degree_file_from_config(config: dict) -> dict: scale_free_params = config["scale-free"] gamma = scale_free_params.get("gamma", None) kmin = int(scale_free_params.get("kmin", scale_free_params.get("loc", 1))) - kmax = scale_free_params.get("kmax", n - 1) + kmax = scale_free_params.get("kmax", None) if kmax is not None: kmax = int(kmax) average_degree = scale_free_params.get("average_degree", None) diff --git a/src/data_creation/spatial_simulation/ml_account_selector.py b/src/data_creation/spatial_simulation/ml_account_selector.py index 54c9ebc2..41119ea8 100644 --- a/src/data_creation/spatial_simulation/ml_account_selector.py +++ b/src/data_creation/spatial_simulation/ml_account_selector.py @@ -419,7 +419,7 @@ def _compute_final_weights(self): # Note: age uses no log (not heavy-tailed), salary/balance use log z_kyc = {} for kyc_feature, weight in self.kyc_weights.items(): - if weight > 0: + if weight != 0: kyc_dict = {} for node in nodes: kyc_value = self.g.nodes[node].get(kyc_feature, 0.0) @@ -437,7 +437,7 @@ def _compute_final_weights(self): # Add structural component (z-scored) for feature, weight in self.structure_weights.items(): - if weight > 0 and feature in z_structural.get(node, {}): + if weight != 0 and feature in z_structural.get(node, {}): score += weight * z_structural[node][feature] # Add propagation component (z-scored global locality fields) @@ -445,12 +445,12 @@ def _compute_final_weights(self): global_key = f"{label_type}_global" prop_weight = self.propagation_weights.get(label_type, 0.0) - if prop_weight > 0 and global_key in z_propagation: + if prop_weight != 0 and global_key in z_propagation: score += prop_weight * z_propagation[global_key].get(node, 0.0) # Add direct KYC component (z-scored) for kyc_feature, weight in self.kyc_weights.items(): - if weight > 0 and kyc_feature in z_kyc: + if weight != 0 and kyc_feature in z_kyc: score += weight * z_kyc[kyc_feature].get(node, 0.0) scores[node] = score diff --git a/src/data_creation/spatial_simulation/transaction_graph_generator.py b/src/data_creation/spatial_simulation/transaction_graph_generator.py index 47abd006..ced631c9 100644 --- a/src/data_creation/spatial_simulation/transaction_graph_generator.py +++ b/src/data_creation/spatial_simulation/transaction_graph_generator.py @@ -638,7 +638,11 @@ def read_normal_models(self, reader): """ header = next(reader) - self.nominator = Nominator(self.g) + # Get participation decay from config (default 1.0 = no decay for backward compatibility) + normal_patterns_conf = self.conf.get('normal_patterns', {}) + participation_decay = normal_patterns_conf.get('participation_decay', 1.0) + + self.nominator = Nominator(self.g, participation_decay=participation_decay) for row in reader: count = int(row[header.index('count')]) diff --git a/src/data_creation/spatial_simulation/utils/nominator.py b/src/data_creation/spatial_simulation/utils/nominator.py index 7610e115..6d82d221 100644 --- a/src/data_creation/spatial_simulation/utils/nominator.py +++ b/src/data_creation/spatial_simulation/utils/nominator.py @@ -1,18 +1,28 @@ import random +from collections import defaultdict class Nominator: """Class responsible for nominating nodes for transactions. - """ - def __init__(self, g): + + Args: + g: NetworkX graph + participation_decay: Factor to multiply node's weight after being selected as main. + Default 1.0 (no decay). Lower values (e.g., 0.5) spread participation more evenly. + """ + def __init__(self, g, participation_decay=1.0): self.g = g + self.participation_decay = participation_decay self.remaining_count_dict = dict() self.used_count_dict = dict() self.model_params_dict = dict() - + self.type_candidates = dict() self.current_candidate_index = dict() self.current_type_index = dict() + # Track selection weights per node (for participation decay) + self.weights = defaultdict(lambda: 1.0) + def initialize_count(self, type, count, schedule_id, min_accounts, max_accounts, min_period, max_period, bank_id): """Counts the number of nodes of a given type. @@ -127,47 +137,68 @@ def has_more(self): def next(self, type): - """Returns the next node id of a given type. + """Returns the next node id of a given type using weighted selection. + + Uses participation decay to spread selections across nodes. Each time a node + is selected as main, its weight is multiplied by participation_decay. Args: type (string): type of node to return Returns: int: node id of next node of given type. - """ - node_id = self.type_candidates[type][self.current_candidate_index[type]] # get next node id from type candidates - - # if fan pattern, double check there are enough neighbors for node_id to be main in the current fan pattern + """ + candidates = self.type_candidates[type] + if not candidates: + self.conclude(type) + return None + + # For fan patterns, filter to candidates meeting structural requirements if type == "fan_in" or type == "fan_out": - current_threshold = self.model_params_dict[type][self.current_type_index[type]][1] - 1 # get threshold for current type - node_fullfill_requirement = not self.is_done(node_id, type, current_threshold) - - start_indx = self.current_candidate_index[type] - - while not node_fullfill_requirement: - self.current_candidate_index[type] += 1 # check the next node in the list of candidates - - if self.current_candidate_index[type] > len(self.type_candidates[type])-1: - self.current_candidate_index[type] = 0 # if we reach the end of the list, start from the beginning - - # if we exhaust the nodes without finding one fullfilling requirements, - if self.current_candidate_index[type] == start_indx: - node_id = None - self.decrement(type) - self.current_type_index[type] += 1 - return node_id - - node_id = self.type_candidates[type][self.current_candidate_index[type]] - node_fullfill_requirement = not self.is_done(node_id, type, current_threshold) - + current_threshold = self.model_params_dict[type][self.current_type_index[type]][1] - 1 + valid_candidates = [n for n in candidates if not self.is_done(n, type, current_threshold)] + else: + valid_candidates = candidates + + if not valid_candidates: + self.decrement(type) + self.current_type_index[type] += 1 + return None + + # Use weighted random selection + node_id = self._weighted_choice(valid_candidates) if node_id is None: self.conclude(type) else: self.decrement(type) self.increment_used(type) + # Apply participation decay + self.weights[node_id] *= self.participation_decay return node_id + def _weighted_choice(self, candidates): + """Select a candidate using weights, respecting participation decay.""" + if not candidates: + return None + + weights = [self.weights[c] for c in candidates] + total = sum(weights) + + if total <= 0: + # All weights decayed to ~0, fall back to uniform + return random.choice(candidates) + + # Normalize and select + r = random.random() * total + cumsum = 0 + for c, w in zip(candidates, weights): + cumsum += w + if r <= cumsum: + return c + + return candidates[-1] # Fallback due to floating point + def types(self): """Returns the types available in normal transactions. @@ -218,18 +249,18 @@ def count(self, type): def post_update(self, node_id, type): - - self.current_type_index[type] += 1 # increment the index of the current type - - if self.is_done(node_id, type): # check if node will be able to be main in other fan_in patterns - self.type_candidates[type].pop(self.current_candidate_index[type]) # remove node_id from list of candidates (if popped, we dont need to increase index) - if len(self.type_candidates[type]) == 0: # if there are no more candidates, conclude the type + """Update candidate list after a node is selected as main. + + Removes the node from candidates if it can no longer be main for this pattern type. + """ + self.current_type_index[type] += 1 # increment the index of the current type + + if self.is_done(node_id, type): # check if node can still be main in other patterns of this type + # Remove node from candidates list + if node_id in self.type_candidates[type]: + self.type_candidates[type].remove(node_id) + if len(self.type_candidates[type]) == 0: # if no more candidates, conclude self.conclude(type) - else: - self.current_candidate_index[type] += 1 # increment index (this is to allow next node considered to be different) - - if self.current_candidate_index[type] >= len(self.type_candidates[type]): # if index is out of bounds, start from beginning - self.current_candidate_index[type] = 0 def is_done(self, node_id, type, threshold=None): diff --git a/src/data_creation/temporal_simulation/alert_patterns.py b/src/data_creation/temporal_simulation/alert_patterns.py index 884b6122..cf240d6f 100644 --- a/src/data_creation/temporal_simulation/alert_patterns.py +++ b/src/data_creation/temporal_simulation/alert_patterns.py @@ -418,6 +418,9 @@ def _generate_scatter_gather(self): Generates random connections between phases and implements amount splitting. Phase 0: originators, Phase 1: intermediaries, Phase 2: beneficiaries Pattern: originators → intermediaries → beneficiaries + + The gather phase amounts are computed as proportions of the total scheduled + incoming to each intermediary, ensuring that outgoing = incoming * (1 - margin_ratio). """ if not self.phase_layers: # Fallback to simple pattern if no phase information @@ -439,49 +442,53 @@ def _generate_scatter_gather(self): # Phase 1: Generate random connections from originators to intermediaries # Each intermediary receives from 1-N originators (random) - intermediary_connections = {} # intermediary -> [(originator, amount, step)] + intermediary_incoming_txs = {} # intermediary -> list of incoming transaction dicts for intermediary in intermediaries: # Each intermediary receives from 1-3 random originators n_sources = min(len(originators), self.random_state.randint(1, 4)) sources = self.random_state.choice(originators, size=n_sources, replace=False) - intermediary_connections[intermediary] = [] + intermediary_incoming_txs[intermediary] = [] for source in sources: amount = self._sample_amount() step = self.random_state.randint(self.start_step, self.start_step + period // 2) - schedule.append({ + tx = { 'step': step, 'from': source, 'to': intermediary, 'amount': amount, 'type': 'TRANSFER' - }) - intermediary_connections[intermediary].append((source, amount, step)) + } + schedule.append(tx) + intermediary_incoming_txs[intermediary].append(tx) # Phase 2: Generate random connections from intermediaries to beneficiaries # Each intermediary splits its total received among 1-M beneficiaries - for intermediary, incoming in intermediary_connections.items(): - # Calculate total received (with margin for fees) - total_received = sum(amt for _, amt, _ in incoming) * (1 - self.margin_ratio) - latest_receive_step = max(step for _, _, step in incoming) + # Outgoing amounts reference the incoming transactions for proportional scaling + for intermediary, incoming_txs in intermediary_incoming_txs.items(): + latest_receive_step = max(tx['step'] for tx in incoming_txs) # Each intermediary sends to 1-3 random beneficiaries n_targets = min(len(beneficiaries), self.random_state.randint(1, 4)) targets = self.random_state.choice(beneficiaries, size=n_targets, replace=False) - # Split total_received randomly among targets using Dirichlet distribution + # Split among targets using Dirichlet distribution (proportions sum to 1) proportions = self.random_state.dirichlet(np.ones(n_targets)) for target, proportion in zip(targets, proportions): - amount = total_received * proportion step = self.random_state.randint(latest_receive_step + 1, self.end_step) + # Mark this as a dependent transaction that references incoming txs + # The amount will be computed as: sum(incoming amounts) * (1 - margin) * proportion schedule.append({ 'step': step, 'from': intermediary, 'to': target, - 'amount': amount, - 'type': 'TRANSFER' + 'amount': None, # Will be computed at execution time + 'type': 'TRANSFER', + '_incoming_refs': incoming_txs, # Reference to incoming transactions + '_proportion': proportion, + '_margin_ratio': self.margin_ratio }) return schedule @@ -493,6 +500,9 @@ def _generate_gather_scatter(self): Generates random connections between phases and implements amount splitting. Phase 0: hubs, Phase 1: sources, Phase 2: targets Pattern: sources → hubs → targets + + The scatter phase amounts are computed as proportions of the total scheduled + incoming to each hub, ensuring that outgoing = incoming * (1 - margin_ratio). """ if not self.phase_layers: # Fallback to simple pattern @@ -516,49 +526,53 @@ def _generate_gather_scatter(self): # Phase 1: Generate random connections from sources to hubs (gather) # Each hub receives from 1-N sources (random) - hub_connections = {} # hub -> [(source, amount, step)] + hub_incoming_txs = {} # hub -> list of incoming transaction dicts for hub in hubs: # Each hub receives from 1-3 random sources n_sources = min(len(sources), self.random_state.randint(1, 4)) senders = self.random_state.choice(sources, size=n_sources, replace=False) - hub_connections[hub] = [] + hub_incoming_txs[hub] = [] for sender in senders: amount = self._sample_amount() step = self.random_state.randint(self.start_step, self.start_step + period // 2) - schedule.append({ + tx = { 'step': step, 'from': sender, 'to': hub, 'amount': amount, 'type': 'TRANSFER' - }) - hub_connections[hub].append((sender, amount, step)) + } + schedule.append(tx) + hub_incoming_txs[hub].append(tx) # Phase 2: Generate random connections from hubs to targets (scatter) # Each hub splits its total received among 1-M targets - for hub, incoming in hub_connections.items(): - # Calculate total received (with margin for fees) - total_received = sum(amt for _, amt, _ in incoming) * (1 - self.margin_ratio) - latest_receive_step = max(step for _, _, step in incoming) + # Outgoing amounts reference the incoming transactions for proportional scaling + for hub, incoming_txs in hub_incoming_txs.items(): + latest_receive_step = max(tx['step'] for tx in incoming_txs) # Each hub sends to 1-3 random targets n_receivers = min(len(targets), self.random_state.randint(1, 4)) receivers = self.random_state.choice(targets, size=n_receivers, replace=False) - # Split total_received randomly among receivers using Dirichlet distribution + # Split among receivers using Dirichlet distribution (proportions sum to 1) proportions = self.random_state.dirichlet(np.ones(n_receivers)) for receiver, proportion in zip(receivers, proportions): - amount = total_received * proportion step = self.random_state.randint(latest_receive_step + 1, self.end_step) + # Mark this as a dependent transaction that references incoming txs + # The amount will be computed as: sum(incoming amounts) * (1 - margin) * proportion schedule.append({ 'step': step, 'from': hub, 'to': receiver, - 'amount': amount, - 'type': 'TRANSFER' + 'amount': None, # Will be computed at execution time + 'type': 'TRANSFER', + '_incoming_refs': incoming_txs, # Reference to incoming transactions + '_proportion': proportion, + '_margin_ratio': self.margin_ratio }) return schedule @@ -597,21 +611,45 @@ def get_transactions_for_step(self, step): def inject_illicit_funds(self): """ - Inject illicit funds into accounts based on source_type. + Inject funds into accounts to ensure pattern transactions can succeed. For CASH patterns: adds cash_balance to the MAIN account only (matches Java behavior) - For TRANSFER patterns: no action needed (funds come from regular balance) - """ - if self.source_type != "CASH": - return + For TRANSFER patterns: ensures all senders have sufficient balance for fixed-amount txs - # Only inject cash to the main account (matches Java's depositCash to 'acct') - if not self.main_accounts: - return + Note: Dependent transactions (amount=None) are not counted here because their + amounts are computed at execution time based on actual successful incoming. + """ + if self.source_type == "CASH": + # Only inject cash to the main account (matches Java's depositCash to 'acct') + if not self.main_accounts: + return - main_account = self.main_accounts[0] + main_account = self.main_accounts[0] - # Calculate total amount that will flow through this pattern - total_amount = sum(tx['amount'] for tx in self.transaction_schedule) + # Calculate total amount from fixed-amount transactions only + total_amount = sum( + tx['amount'] for tx in self.transaction_schedule + if tx.get('amount') is not None + ) - # Inject cash to the main account (add safety margin) - main_account.cash_balance = max(total_amount * 1.5, main_account.balance * 0.5) + # Inject cash to the main account (add safety margin) + main_account.cash_balance = max(total_amount * 1.5, main_account.balance * 0.5) + else: + # TRANSFER: Ensure all senders have sufficient balance for their transactions + # Only count fixed-amount transactions; dependent transactions (amount=None) + # will use whatever the sender actually received from successful incoming. + sender_amounts = {} + for tx in self.transaction_schedule: + amount = tx.get('amount') + if amount is None: + # Skip dependent transactions - they're computed at execution time + continue + sender = tx['from'] + if sender not in sender_amounts: + sender_amounts[sender] = 0 + sender_amounts[sender] += amount + + # Add balance to senders that need it (with safety margin) + for sender, total_needed in sender_amounts.items(): + if sender.balance < total_needed: + shortfall = total_needed - sender.balance + sender.balance += shortfall * 1.5 # Add 50% safety margin diff --git a/src/data_creation/temporal_simulation/simulator.py b/src/data_creation/temporal_simulation/simulator.py index dc808f92..945b2599 100644 --- a/src/data_creation/temporal_simulation/simulator.py +++ b/src/data_creation/temporal_simulation/simulator.py @@ -502,12 +502,39 @@ def execute_alert_transactions(self, step): for tx in scheduled_txs: from_account = tx['from'] to_account = tx['to'] - amount = tx['amount'] tx_type = tx['type'] + # Handle dependent transactions (scatter phase of gather-scatter) + # These have amount=None and reference incoming transactions + if tx.get('amount') is None and '_incoming_refs' in tx: + # Compute amount based on actual successful incoming transactions + incoming_refs = tx['_incoming_refs'] + proportion = tx['_proportion'] + margin_ratio = tx['_margin_ratio'] + + # Sum the actual amounts from successful incoming transactions + total_incoming = sum( + ref.get('_actual_amount', 0) + for ref in incoming_refs + if ref.get('_success', False) + ) + + # Compute outgoing amount: total * (1 - margin) * proportion + amount = total_incoming * (1 - margin_ratio) * proportion + + if amount <= 0: + # No successful incoming, skip this outgoing transaction + continue + else: + amount = tx['amount'] + # Attempt the transaction success = from_account.make_transaction(to_account, amount, tx_type) + # Track success and actual amount for dependent transactions + tx['_success'] = success + tx['_actual_amount'] = amount if success else 0 + if success: transactions.append({ 'step': step, diff --git a/tests/data_creation/parameters/large_test/config/accounts.csv b/tests/data_creation/parameters/large_test/config/accounts.csv index bae5aad0..116b7c96 100644 --- a/tests/data_creation/parameters/large_test/config/accounts.csv +++ b/tests/data_creation/parameters/large_test/config/accounts.csv @@ -1,2 +1,2 @@ count,country,business_type,bank_id -100000,SWE,I,bank +10000,SWE,I,bank diff --git a/tests/data_creation/parameters/large_test/config/degree.csv b/tests/data_creation/parameters/large_test/config/degree.csv index 253729c6..a12c7e55 100644 --- a/tests/data_creation/parameters/large_test/config/degree.csv +++ b/tests/data_creation/parameters/large_test/config/degree.csv @@ -1,436 +1,17 @@ Count,In-degree,Out-degree -40,0,1 -13764,0,2 -4695,0,3 -967,0,4 -290,0,5 -146,0,6 -69,0,7 -43,0,8 -24,0,9 -6,0,10 -9,0,11 -7,0,12 -3,0,13 -2,0,14 -3,0,15 -1,0,16 -1,0,18 -2,0,19 -1,0,21 -2,0,22 -1,0,23 -1,0,55 -1,0,68 -27913,1,1 -9425,1,2 -1944,1,3 -623,1,4 -280,1,5 -123,1,6 -82,1,7 -50,1,8 -25,1,9 -20,1,10 -13,1,11 -15,1,12 -7,1,13 -5,1,14 -8,1,15 -4,1,16 -1,1,17 -1,1,18 -2,1,19 -1,1,20 -1,1,21 -1,1,25 -1,1,26 -1,1,32 -1,1,34 -1,1,45 -13844,2,0 -9586,2,1 -1921,2,2 -677,2,3 -286,2,4 -130,2,5 -71,2,6 -51,2,7 -22,2,8 -18,2,9 -10,2,10 -7,2,11 -5,2,12 -4,2,13 -7,2,14 -5,2,15 -3,2,16 -1,2,17 -1,2,18 -2,2,19 -1,2,21 -1,2,22 -1,2,29 -2,2,30 -4778,3,0 -1956,3,1 -687,3,2 -249,3,3 -130,3,4 -76,3,5 -38,3,6 -35,3,7 -22,3,8 -18,3,9 -10,3,10 -3,3,11 -2,3,12 -5,3,13 -6,3,14 -1,3,15 -2,3,16 -1,3,17 -1,3,18 -3,3,19 -2,3,20 -2,3,21 -1,3,22 -1,3,26 -1,3,31 -1,3,36 -1,3,44 -1,3,52 -970,4,0 -660,4,1 -260,4,2 -140,4,3 -69,4,4 -48,4,5 -36,4,6 -24,4,7 -14,4,8 -5,4,9 -5,4,10 -8,4,11 -3,4,12 -3,4,13 -2,4,14 -5,4,15 -1,4,17 -3,4,21 -1,4,24 -1,4,34 -331,5,0 -275,5,1 -133,5,2 -76,5,3 -40,5,4 -25,5,5 -24,5,6 -17,5,7 -7,5,8 -6,5,9 -4,5,10 -3,5,11 -3,5,12 -3,5,13 -1,5,16 -1,5,18 -1,5,31 -1,5,34 -129,6,0 -144,6,1 -84,6,2 -57,6,3 -24,6,4 -17,6,5 -12,6,6 -8,6,7 -7,6,8 -5,6,9 -3,6,10 -5,6,12 -1,6,13 -1,6,14 -2,6,16 -1,6,17 -1,6,18 -1,6,32 -57,7,0 -55,7,1 -30,7,2 -32,7,3 -10,7,4 -16,7,5 -8,7,6 -4,7,7 -6,7,8 -3,7,9 -2,7,10 -3,7,11 -1,7,12 -2,7,15 -2,7,16 -1,7,18 -1,7,22 -1,7,23 -1,7,26 -1,7,34 -1,7,38 -1,7,42 -1,7,52 -26,8,0 -49,8,1 -33,8,2 -12,8,3 -17,8,4 -7,8,5 -6,8,6 -5,8,7 -3,8,8 -1,8,9 -3,8,12 -2,8,13 -1,8,14 -1,8,15 -1,8,21 -2,8,22 -1,8,23 -1,8,24 -1,8,26 -1,8,28 -1,8,34 -1,8,56 -1,8,76 -21,9,0 -23,9,1 -29,9,2 -11,9,3 -10,9,4 -7,9,5 -5,9,6 -1,9,7 -7,9,8 -1,9,9 -4,9,10 -2,9,11 -1,9,12 -1,9,14 -1,9,15 -1,9,16 -3,9,18 -1,9,19 -1,9,23 -1,9,24 -1,9,28 -1,9,33 -15,10,0 -9,10,1 -12,10,2 -12,10,3 -6,10,4 -4,10,5 -6,10,6 -3,10,7 -3,10,8 -4,10,9 -2,10,10 -1,10,11 -2,10,12 -1,10,13 -1,10,16 -1,10,28 -1,10,36 -8,11,0 -12,11,1 -10,11,2 -4,11,3 -3,11,4 -4,11,5 -2,11,6 -2,11,7 -2,11,8 -1,11,10 -1,11,12 -2,11,13 -1,11,14 -1,11,31 -5,12,0 -9,12,1 -7,12,2 -8,12,3 -1,12,4 -2,12,5 -3,12,6 -2,12,7 -1,12,8 -2,12,9 -1,12,10 -2,12,11 -1,12,13 -2,12,21 -2,12,37 -5,13,0 -6,13,1 -3,13,2 -5,13,3 -2,13,4 -3,13,5 -2,13,6 -1,13,7 -1,13,9 -1,13,10 -1,13,12 -1,13,14 -1,13,16 -1,13,18 -1,13,26 -1,13,33 -1,13,40 -2,14,0 -5,14,1 -1,14,2 -3,14,3 -5,14,4 -1,14,6 -1,14,7 -1,14,11 -1,14,12 -1,14,16 -1,14,19 -1,14,25 -2,15,0 -4,15,1 -5,15,2 -1,15,3 -2,15,4 -1,15,5 -1,15,7 -1,15,8 -1,15,28 -1,15,45 -1,15,58 -1,16,0 -6,16,1 -5,16,3 -2,16,5 -2,16,6 -3,16,7 -1,16,54 -1,17,0 -1,17,1 -1,17,3 -1,17,5 -3,17,6 -1,17,7 -1,17,9 -2,17,10 -2,17,12 -1,17,30 -3,18,0 -2,18,2 -1,18,10 -1,18,11 -1,18,12 -1,18,14 -2,18,19 -1,18,34 -1,18,37 -1,18,160 -2,19,1 -2,19,2 -3,19,3 -1,19,4 -1,19,6 -1,19,8 -1,19,15 -1,19,22 -1,19,24 -1,19,26 -1,19,28 -1,19,48 -4,20,0 -2,20,1 -2,20,2 -2,20,3 -1,20,41 -1,20,138 -3,21,2 -1,21,3 -1,21,4 -2,21,5 -1,21,8 -1,21,28 -2,22,0 -2,22,2 -1,22,3 -1,22,4 -1,22,8 -1,22,15 -1,22,16 -1,22,35 -1,23,0 -1,23,5 -1,23,7 -1,23,14 -1,23,30 -2,24,1 -1,24,2 -1,24,6 -1,24,11 -1,25,1 -1,25,2 -1,26,1 -1,26,5 -1,26,32 -1,26,40 -1,27,6 -1,27,14 -1,27,57 -1,28,2 -1,28,3 -2,28,10 -1,28,41 -1,28,46 -2,29,1 -1,29,3 -1,29,7 -1,29,10 -1,29,41 -1,29,83 -1,32,3 -1,33,0 -1,33,38 -1,34,2 -1,34,11 -1,35,12 -1,35,40 -1,35,50 -1,36,4 -1,37,35 -1,38,7 -1,39,2 -1,39,11 -1,40,2 -1,40,82 -1,42,13 -1,42,111 -1,44,119 -1,46,18 -1,46,26 -1,48,6 -1,52,9 -1,60,51 -1,61,80 -1,62,78 -1,64,11 -1,64,12 -1,64,16 -1,67,12 -1,83,4 -1,96,26 -1,112,56 -1,117,37 -1,126,17 -1,126,32 -1,205,9 +4000,1,1 +2500,2,2 +1500,3,3 +800,4,4 +400,5,5 +300,6,6 +200,7,7 +100,8,8 +80,9,9 +50,10,10 +30,11,11 +20,12,12 +10,15,15 +5,20,20 +3,25,25 +2,30,30 diff --git a/tests/data_creation/unit/spatial/test_generate_scalefree.py b/tests/data_creation/unit/spatial/test_generate_scalefree.py index d7476f2c..f384ad03 100644 --- a/tests/data_creation/unit/spatial/test_generate_scalefree.py +++ b/tests/data_creation/unit/spatial/test_generate_scalefree.py @@ -340,13 +340,13 @@ def test_raises_for_kmax_less_than_kmin(self): n=100, kmin=10, kmax=5, average_degree=5.0, seed=42 ) - def test_default_kmax_is_n_minus_1(self): - """Default kmax should be n-1.""" + def test_default_kmax_is_sqrt_n(self): + """Default kmax should be floor(sqrt(n)).""" n = 100 _, _, stats = discrete_powerlaw_degree_distribution( - n=n, kmin=1, average_degree=5.0, seed=42 + n=n, kmin=1, average_degree=3.0, seed=42 ) - assert stats['kmax'] == n - 1 + assert stats['kmax'] == int(np.floor(np.sqrt(n))) # 10 for n=100 def test_reproducible_with_seed(self): """Same seed should give same results.""" @@ -458,7 +458,7 @@ def test_legacy_loc_parameter(self, tmp_path): 'spatial': {'directory': str(spatial_dir)}, 'scale-free': { 'loc': 2, # Legacy parameter - 'average_degree': 5.0 + 'average_degree': 3.0 # Lower target for kmax=sqrt(100)=10 } } diff --git a/tests/data_creation/unit/spatial/test_nominator.py b/tests/data_creation/unit/spatial/test_nominator.py index fa3a9640..434b4a7c 100644 --- a/tests/data_creation/unit/spatial/test_nominator.py +++ b/tests/data_creation/unit/spatial/test_nominator.py @@ -453,23 +453,6 @@ def test_post_update_removes_done_node(self, nominator): assert len(nominator.type_candidates['fan_out']) == initial_len - 1 assert current_node not in nominator.type_candidates['fan_out'] - def test_post_update_wraps_index(self, nominator): - """Test that post_update wraps candidate index""" - nominator.initialize_count('single', 1, 1, 2, 3, 1, 100, 'bank_1') - nominator.initialize_candidates() - - # Set index to last position - nominator.current_candidate_index['single'] = len(nominator.type_candidates['single']) - 1 - - # Mock is_done to return False - nominator.is_done = MagicMock(return_value=False) - - nominator.post_update(0, 'single') - - # Index should wrap to 0 - assert nominator.current_candidate_index['single'] == 0 - - @pytest.mark.unit @pytest.mark.spatial class TestNominatorIsDoneMethods: diff --git a/tutorial.ipynb b/tutorial.ipynb index 70a7b2f3..01339b31 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -57,19 +57,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project root: /Users/jostman/Projects/AMLGentex\n", - "Experiment: tutorial_demo\n", - "Experiment root: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "import sys\n", @@ -105,17 +95,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Created experiment structure at: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo\n" - ] - } - ], + "outputs": [], "source": [ "# Create experiment directories\n", "config_dir = experiment_root / \"config\"\n", @@ -133,21 +115,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Copied data.yaml from /Users/jostman/Projects/AMLGentex/experiments/template_experiment/config\n", - "Copied preprocessing.yaml from /Users/jostman/Projects/AMLGentex/experiments/template_experiment/config\n", - "Copied models.yaml from /Users/jostman/Projects/AMLGentex/experiments/template_experiment/config\n", - "\n", - "✓ Updated simulation_name to: tutorial_demo\n" - ] - } - ], + "outputs": [], "source": [ "# Instead of creating from scratch, copy the working config from template\n", "import shutil\n", @@ -190,22 +160,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Copied accounts.csv\n", - "✓ Copied alertPatterns.csv\n", - "✓ Copied normalModels.csv\n", - "⚠ Warning: degree.csv not found in template\n", - "✓ Copied transactionType.csv\n", - "✓ Copied demographics.csv\n" - ] - } - ], + "outputs": [], "source": [ "# Use existing 10k_accounts config as a template for supporting files\n", "template_dir = project_root / \"experiments\" / \"template_experiment\" / \"config\"\n", @@ -245,123 +202,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Configuration loaded with auto-discovered paths:\n", - " Input dir: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/config\n", - " Output dir: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/temporal\n", - "\n", - "Generating synthetic transaction data...\n", - "This may take a few minutes...\n", - "\n", - "INFO - src.data_creation.generator - Running spatial simulation...\n", - "INFO - src.data_creation.generator - [1/3] Generating degree distribution...\n", - "INFO - src.data_creation.generator - Complete (0.01s)\n", - "INFO - src.data_creation.generator - [2/3] Generating transaction graph...\n", - "INFO - spatial_simulation.transaction_graph_generator - Random seed: 0\n", - "INFO - spatial_simulation.transaction_graph_generator - Simulation name: tutorial_demo\n", - "INFO - spatial_simulation.transaction_graph_generator - Add 21084 base transactions\n", - "INFO - spatial_simulation.transaction_graph_generator - Generated 10000 accounts.\n", - "INFO - spatial_simulation.transaction_graph_generator - Generated 22654 normal models.\n", - "INFO - spatial_simulation.transaction_graph_generator - Normal model counts {'single': 5000, 'fan_out': 1361, 'fan_in': 1293, 'forward': 5000, 'mutual': 5000, 'periodical': 5000}\n", - "INFO - spatial_simulation.transaction_graph_generator - Assigning demographics-based KYC from /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/config/demographics.csv...\n", - "INFO - spatial_simulation.demographics_assigner - Detected single-year age format\n", - "INFO - spatial_simulation.demographics_assigner - Population median monthly salary: 35308 SEK\n", - "INFO - spatial_simulation.demographics_assigner - Initialized DemographicsAssigner with 85 age groups\n", - "INFO - spatial_simulation.demographics_assigner - Assigning demographics-based KYC to nodes...\n", - "INFO - spatial_simulation.demographics_assigner - Assigned 10 cities to 10000 nodes\n", - "INFO - spatial_simulation.demographics_assigner - ============================================================\n", - "INFO - spatial_simulation.demographics_assigner - Demographics Assignment Statistics\n", - "INFO - spatial_simulation.demographics_assigner - ============================================================\n", - "INFO - spatial_simulation.demographics_assigner - Age: mean=49.3, std=19.7, range=[16, 100]\n", - "INFO - spatial_simulation.demographics_assigner - Salary (monthly SEK): mean=27955, median=26461, std=16040\n", - "INFO - spatial_simulation.demographics_assigner - Balance: mean=130643, median=88271, std=542600\n", - "INFO - spatial_simulation.demographics_assigner - Correlations with log(balance):\n", - "INFO - spatial_simulation.demographics_assigner - age: 0.181\n", - "INFO - spatial_simulation.demographics_assigner - log(salary): 0.670\n", - "INFO - spatial_simulation.demographics_assigner - structural_z: 0.472\n", - "INFO - spatial_simulation.demographics_assigner - Cities: 10 unique, sizes min=378, max=2212, mean=1000\n", - "INFO - spatial_simulation.demographics_assigner - ============================================================\n", - "INFO - spatial_simulation.demographics_assigner - Assigned KYC to 10000 nodes\n", - "INFO - spatial_simulation.transaction_graph_generator - Demographics assignment complete\n", - "INFO - spatial_simulation.transaction_graph_generator - Preparing money laundering account selector...\n", - "INFO - spatial_simulation.ml_account_selector - Initialized ML Account Selector with config: {'n_target_labels': 5, 'n_seeds_per_target': 10, 'restart_alpha': 0.15, 'seed_strategy': 'degree', 'propagate_labels': ['city'], 'structure_weights': {'degree': 0.4, 'betweenness': 0.3, 'pagerank': 0.8}, 'propagation_weights': {'city': 1.0}, 'kyc_weights': {'init_balance': 0.0, 'salary': 0.0, 'age': 0.0}}\n", - "INFO - spatial_simulation.ml_account_selector - Preparing ML account selector...\n", - "INFO - spatial_simulation.ml_account_selector - Computing structural metrics...\n", - "INFO - spatial_simulation.ml_account_selector - Computed approximate betweenness with k=500 samples, seed=850624225\n", - "INFO - spatial_simulation.ml_account_selector - Computed structural metrics for 10000 nodes\n", - "INFO - spatial_simulation.ml_account_selector - Selecting target labels and seeds...\n", - "INFO - spatial_simulation.ml_account_selector - Selected 5 target citys: ['city_3' 'city_1' 'city_0' 'city_6' 'city_8']\n", - "INFO - spatial_simulation.ml_account_selector - Selected 50 total seeds across 5 target groups\n", - "INFO - spatial_simulation.ml_account_selector - Propagating labels via Personalized PageRank toward targets...\n", - "INFO - spatial_simulation.ml_account_selector - Combined 5 target PPR fields into 'city_global'\n", - "INFO - spatial_simulation.ml_account_selector - Computing final ML selection weights...\n", - "INFO - spatial_simulation.ml_account_selector - Computed final weights for 10000 nodes\n", - "INFO - spatial_simulation.ml_account_selector - Caching bank-specific weights...\n", - "INFO - spatial_simulation.ml_account_selector - Cached weights for 1 banks\n", - "INFO - spatial_simulation.ml_account_selector - ML account selector preparation complete\n", - "INFO - spatial_simulation.ml_account_selector - ============================================================\n", - "INFO - spatial_simulation.ml_account_selector - ML Account Selector Statistics\n", - "INFO - spatial_simulation.ml_account_selector - ============================================================\n", - "INFO - spatial_simulation.ml_account_selector - Weight statistics: min=0.0000, max=1.0000, mean=0.0001, std=0.0100\n", - "INFO - spatial_simulation.ml_account_selector - Degree statistics: min=1, max=331, mean=4.22\n", - "INFO - spatial_simulation.ml_account_selector - Betweenness statistics: min=0.000000, max=0.180520, mean=0.000428\n", - "INFO - spatial_simulation.ml_account_selector - Target citys (5): [np.str_('city_3'), np.str_('city_1'), np.str_('city_0'), np.str_('city_6'), np.str_('city_8')]\n", - "INFO - spatial_simulation.ml_account_selector - Propagation 'city_global': min=0.000005, max=0.006757, mean=0.000100\n", - "INFO - spatial_simulation.ml_account_selector - Total seeds: 50 across 5 target groups\n", - "INFO - spatial_simulation.ml_account_selector - city:city_3: 10 seeds\n", - "INFO - spatial_simulation.ml_account_selector - city:city_1: 10 seeds\n", - "INFO - spatial_simulation.ml_account_selector - city:city_0: 10 seeds\n", - "INFO - spatial_simulation.ml_account_selector - city:city_6: 10 seeds\n", - "INFO - spatial_simulation.ml_account_selector - city:city_8: 10 seeds\n", - "INFO - spatial_simulation.ml_account_selector - ============================================================\n", - "INFO - spatial_simulation.transaction_graph_generator - ML account selector ready\n", - "INFO - spatial_simulation.transaction_graph_generator - Propagated SAR flags to 572 accounts from 140 alert patterns\n", - "INFO - spatial_simulation.transaction_graph_generator - Exported 10000 accounts to /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/accounts.csv\n", - "INFO - spatial_simulation.transaction_graph_generator - Exported 22694 transactions to /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/transactions.csv\n", - "INFO - spatial_simulation.transaction_graph_generator - Output alert model list to: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/alert_models.csv\n", - "INFO - spatial_simulation.transaction_graph_generator - Exported 1212 members for 140 AML typologies to /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/alert_models.csv\n", - "INFO - src.data_creation.generator - Complete (7.75s)\n", - "INFO - src.data_creation.generator - [3/3] No patterns to insert\n", - "INFO - src.data_creation.generator - Running temporal simulation...\n", - "INFO - temporal_simulation.simulator - Initialized AMLSimulator: tutorial_demo\n", - "INFO - temporal_simulation.simulator - Random seed: 0\n", - "INFO - temporal_simulation.simulator - Total steps: 100\n", - "INFO - temporal_simulation.simulator - Loading accounts from: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/accounts.csv\n", - "INFO - temporal_simulation.simulator - Loaded 10000 accounts\n", - "INFO - temporal_simulation.simulator - Loading transactions from: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/transactions.csv\n", - "INFO - temporal_simulation.simulator - Loaded 18460 transaction connections\n", - "INFO - temporal_simulation.simulator - Loading normal models from: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/normal_models.csv\n", - "INFO - temporal_simulation.simulator - Loaded 61180 normal model entries\n", - "INFO - temporal_simulation.simulator - Created 22654 normal model objects\n", - "INFO - temporal_simulation.simulator - Loading alert models from: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/spatial/alert_models.csv\n", - "INFO - temporal_simulation.simulator - Loaded 1212 alert memberships\n", - "INFO - temporal_simulation.simulator - Created 140 alert patterns\n", - "INFO - temporal_simulation.simulator - \n", - "Starting AMLSim for 100 steps...\n", - "INFO - temporal_simulation.simulator - ============================================================\n", - "INFO - temporal_simulation.simulator - Available banks: 1\n", - "INFO - temporal_simulation.simulator - Step 0/100\n", - "INFO - temporal_simulation.simulator - ============================================================\n", - "INFO - temporal_simulation.simulator - Simulation complete! Generated 604436 transactions\n", - "INFO - temporal_simulation.simulator - Writing output to: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/temporal/tx_log.parquet\n", - "INFO - temporal_simulation.simulator - Wrote 604436 transactions to /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/temporal/tx_log.parquet\n", - "INFO - src.data_creation.generator - Complete: 604,436 transactions in 2.38s\n", - "\n", - "✓ Transaction data generated: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/temporal/tx_log.parquet\n", - "\n", - "Generated 604,436 transactions\n", - "Banks: ['source', 'bank_a']\n", - "SAR transactions: 1,480 (0.24%)\n", - "Time range: steps 0 to 99\n" - ] - } - ], + "outputs": [], "source": [ "from src.data_creation import DataGenerator\n", "from src.utils.config import load_data_config\n", @@ -461,153 +304,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "DATA OPTIMIZATION\n", - "============================================================\n", - "\n", - "Objective: Optimize precision in top 100 reported alerts\n", - "Target: 2.0% precision\n", - "Data optimization trials: 100\n", - "Model hyperparameter trials per data config: 20\n", - "\n", - "Optimizer will directly modify: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/config/data.yaml\n", - "\n", - "Initializing data optimizer...\n", - "Two-phase generation:\n", - " 1. Generate baseline (normal accounts + demographics) - ONCE\n", - " 2. For each trial:\n", - " a. Inject alerts from baseline with trial's ML selector weights\n", - " b. Run temporal simulation\n", - " c. Preprocess into features\n", - " d. Tune DecisionTreeClassifier hyperparameters (20 trials)\n", - " e. Measure precision and feature stability\n", - "\n", - "Starting Bayesian optimization (100 data trials × 20 model trials each)...\n", - "This may take some time depending on data size and number of trials.\n", - "\n", - "Generating baseline graph (Phase 1)...\n", - "Trial 1/100: precision=0.920, loss=0.900\n", - "Trial 2/100: precision=0.920, loss=0.900\n", - "Trial 3/100: precision=0.180, loss=0.160\n", - "Trial 4/100: precision=0.160, loss=0.140\n", - "Trial 5/100: precision=0.110, loss=0.090\n", - "Trial 6/100: precision=0.870, loss=0.850\n", - "Trial 7/100: precision=0.470, loss=0.450\n", - "Trial 8/100: precision=0.630, loss=0.610\n", - "Trial 9/100: precision=0.270, loss=0.250\n", - "Trial 10/100: precision=0.130, loss=0.110\n", - "Trial 11/100: precision=0.810, loss=0.790\n", - "Trial 12/100: precision=0.850, loss=0.830\n", - "Trial 13/100: precision=0.740, loss=0.720\n", - "Trial 14/100: precision=0.100, loss=0.080\n", - "Trial 15/100: precision=0.120, loss=0.100\n", - "Trial 16/100: precision=0.070, loss=0.050\n", - "Trial 17/100: precision=0.100, loss=0.080\n", - "Trial 18/100: precision=0.090, loss=0.070\n", - "Trial 19/100: precision=0.120, loss=0.100\n", - "Trial 20/100: precision=0.110, loss=0.090\n", - "Trial 21/100: precision=0.140, loss=0.120\n", - "Trial 22/100: precision=0.110, loss=0.090\n", - "Trial 23/100: precision=0.060, loss=0.040\n", - "Trial 24/100: precision=0.420, loss=0.400\n", - "Trial 25/100: precision=0.320, loss=0.300\n", - "Trial 26/100: precision=0.200, loss=0.180\n", - "Trial 27/100: precision=0.100, loss=0.080\n", - "Trial 28/100: precision=0.110, loss=0.090\n", - "Trial 29/100: precision=0.120, loss=0.100\n", - "Trial 30/100: precision=0.430, loss=0.410\n", - "Trial 31/100: precision=0.030, loss=0.010\n", - "Trial 32/100: precision=0.130, loss=0.110\n", - "Trial 33/100: precision=0.190, loss=0.170\n", - "Trial 34/100: precision=0.150, loss=0.130\n", - "Trial 35/100: precision=0.130, loss=0.110\n", - "Trial 36/100: precision=0.070, loss=0.050\n", - "Trial 37/100: precision=0.260, loss=0.240\n", - "Trial 38/100: precision=0.190, loss=0.170\n", - "Trial 39/100: precision=0.440, loss=0.420\n", - "Trial 40/100: precision=0.090, loss=0.070\n", - "Trial 41/100: precision=0.220, loss=0.200\n", - "Trial 42/100: precision=0.090, loss=0.070\n", - "Trial 43/100: precision=0.310, loss=0.290\n", - "Trial 44/100: precision=0.130, loss=0.110\n", - "Trial 45/100: precision=0.670, loss=0.650\n", - "Trial 46/100: precision=0.420, loss=0.400\n", - "Trial 47/100: precision=0.090, loss=0.070\n", - "Trial 48/100: precision=0.110, loss=0.090\n", - "Trial 49/100: precision=0.080, loss=0.060\n", - "Trial 50/100: precision=0.400, loss=0.380\n", - "Trial 51/100: precision=0.210, loss=0.190\n", - "Trial 52/100: precision=0.270, loss=0.250\n", - "Trial 53/100: precision=0.090, loss=0.070\n", - "Trial 54/100: precision=0.150, loss=0.130\n", - "Trial 55/100: precision=0.130, loss=0.110\n", - "Trial 56/100: precision=0.190, loss=0.170\n", - "Trial 57/100: precision=0.120, loss=0.100\n", - "Trial 58/100: precision=0.480, loss=0.460\n", - "Trial 59/100: precision=0.260, loss=0.240\n", - "Trial 60/100: precision=0.060, loss=0.040\n", - "Trial 61/100: precision=0.250, loss=0.230\n", - "Trial 62/100: precision=0.100, loss=0.080\n", - "Trial 63/100: precision=0.130, loss=0.110\n", - "Trial 64/100: precision=0.210, loss=0.190\n", - "Trial 65/100: precision=0.150, loss=0.130\n", - "Trial 66/100: precision=0.100, loss=0.080\n", - "Trial 67/100: precision=0.250, loss=0.230\n", - "Trial 68/100: precision=0.140, loss=0.120\n", - "Trial 69/100: precision=0.080, loss=0.060\n", - "Trial 70/100: precision=0.110, loss=0.090\n", - "Trial 71/100: precision=0.640, loss=0.620\n", - "Trial 72/100: precision=0.120, loss=0.100\n", - "Trial 73/100: precision=0.040, loss=0.020\n", - "Trial 74/100: precision=0.200, loss=0.180\n", - "Trial 75/100: precision=0.060, loss=0.040\n", - "Trial 76/100: precision=0.400, loss=0.380\n", - "Trial 77/100: precision=0.430, loss=0.410\n", - "Trial 78/100: precision=0.390, loss=0.370\n", - "Trial 79/100: precision=0.210, loss=0.190\n", - "Trial 80/100: precision=0.320, loss=0.300\n", - "Trial 81/100: precision=0.070, loss=0.050\n", - "Trial 82/100: precision=0.100, loss=0.080\n", - "Trial 83/100: precision=0.060, loss=0.040\n", - "Trial 84/100: precision=0.110, loss=0.090\n", - "Trial 85/100: precision=0.130, loss=0.110\n", - "Trial 86/100: precision=0.130, loss=0.110\n", - "Trial 87/100: precision=0.100, loss=0.080\n", - "Trial 88/100: precision=0.090, loss=0.070\n", - "Trial 89/100: precision=0.320, loss=0.300\n", - "Trial 90/100: precision=0.060, loss=0.040\n", - "Trial 91/100: precision=0.140, loss=0.120\n", - "Trial 92/100: precision=0.100, loss=0.080\n", - "Trial 93/100: precision=0.240, loss=0.220\n", - "Trial 94/100: precision=0.080, loss=0.060\n", - "Trial 95/100: precision=0.160, loss=0.140\n", - "Trial 96/100: precision=0.320, loss=0.300\n", - "Trial 97/100: precision=0.170, loss=0.150\n", - "Trial 98/100: precision=0.090, loss=0.070\n", - "Trial 99/100: precision=0.220, loss=0.200\n", - "Trial 100/100: precision=0.110, loss=0.090\n", - "\n", - "============================================================\n", - "OPTIMIZATION COMPLETE\n", - "============================================================\n", - "\n", - "Found 7 Pareto-optimal solutions\n", - "Results saved to: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/data_tuning\n", - " - pareto_front.png: Visualization of trade-offs\n", - " - best_trials.txt: Parameter values for each solution\n", - " - data_tuning_study.db: Full optimization history\n", - "\n", - "Note: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/config/data.yaml now contains parameters from the last trial.\n" - ] - } - ], + "outputs": [], "source": [ "from src.data_tuning import DataTuner\n", "from src.data_creation import DataGenerator\n", @@ -750,217 +449,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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j2aQ33ngjYkB11VVXtarRkV2FT5xox2RdJ1t/RFD96oHwOtnmTsRopky/tGO0Vaw67nRebbgE+Sc/+Yl88MEHzWbVH3zwwUZ/v6L5HdN50zpSS+nvtXa8juT3v/993f6ceeaZdSc+7JoBPu200+oywLrvkejPOnv27Lr3XDuCJ0os7wkA72ANMADEmWavVqxYcUAgVlpaasamaObq+eefN+sdw8477zyZNWtWg+xvuDGPZnc1GLjyyivlk08+MRkTXbuopdP64VkznPpBUkuBmwr2TjrpJDMr89NPPzUf+vV5649d0jV7Ok9VSxJHjBhhRtC89tprZl2yBlx9+/Y1ZdZvvvmmCQD0Zxo9erSZh2qFn/3sZ2aGq742msW75ZZbzAdwfR01+HvooYfMz6BzZrW81U00sH/qqadMoPHKK6+YbsvXX3+9HHbYYbJv3z6TGb7//vtNkzQdJbN69Wrbl4Naddzpca9r7S+55BKzjv7EE0+UiRMnmtm0epuOKtLfLf2d1jXKumZff/+uu+66Ax5H913nCOu+/uEPf5Dc3FyzpljfA6VN7LRRndJjVLPzJ598svn7oM+tP7Oufdau3drMTm8vLCysq3h45JFHxO70RJy+DjqSSP/G6Pxi/RumP7f+bdJ13U8++aTZVv/W6PeR/u7FSyzvCQAPCQEAWu2rr77SdFVMX4MHDw499dRTzT72/fffH/L5fI0+zsCBA0Nr1qwJ9erVy/z75JNPjvg4X3zxRSgnJ6fRx9GfIWzlypWhbt26NbptRkZG6LHHHgvdcccdEe/fWpdffnnd47755puNbnfnnXc2uo/6mv3mN78JPfHEE00+Viw/g762up2+1pE09x40d3uk40n3L5Li4uLQMccc0+jP36FDh9B//vOf0Iknnlh3vDX3XI39XPF+3xoTr+Ou/n5E64UXXgh16dIlqt/doUOHRnyM3/72t43eJ9J7/sorr4Q6duzY5HP17Nkz9Nlnn4Vao/7rpb8P0YrmODzYJ598Ejr00EOb/Jk6deoUeu211xp9jHj+nsT6ngBwP0qgASDBdB2ajkzRLK1mKn/729+azN3KlSsjlj0fTDN7b731lpx//vkmO6QZKc1kHHfccXLfffeZjK420GrOkCFDTHZNs4c6bkmzH41lBfX2pUuXyk033WS+17mdOpNTyzanTJliMtGakbaajsN5+eWX5Xvf+555TTSrdMghh5hM2ttvvy133HGHuJV2z9Xj6K9//auMGjXKZPP1PdX3SMdlffbZZyYjHu62Wz/bb1dWHndaiqvl4Lo+9dxzz5VDDz3UPL8eU5rx1d+3a6+91lRw6GsbyW233WaynFqlofN7w5nGxujMY63K+N3vfmfeQ31Pw8+nJf4PPPCAfPnll7ZufhUp66oVB9OnTzc/g/4s+jPpz6Y/o/6s+jOfccYZSdmfWN8TAO7n0yjY6p0AAADxp41/NPDVS52xGy4/BQDAq8gAAwDgUnPmzKnrfqvrZgEA8DoywAAAOJA2StKy7/qjXurTEnttslRUVGTKiHVckhPKoAEASCS6QAMA4EAvvPCCWd+o6521w3efPn3M+nANdLWjsHY2Ds/Tveeeewh+AQAgAwwAgDP97W9/k2uuuabJbXTEjDYKc3MzMAAAYkEGGAAAB9Ku4NrFW7O9n3/+uRQXF5uOzzr3VGebavmzBsja/RsAANRiDTAAAAAAwBPoAg0AAAAA8AQCYAAAAACAJxAAAwAAAAA8gQAYAAAAAOAJBMAAAAAAAE8gAAYAAAAAeAIBMAAAAADAE1Kt3gFEb9u2bRIKhZLykuXm5prL4uLipDwf0BIcp3ACjlM4AccpnIDj1Bt8Pp9069YtYY9PAOwgGvwmKwCu/5yA3XGcwgk4TuEEHKdwAo5TtAYl0AAAAAAATyAABgAAAAB4AgEwAAAAAMATCIABAAAAAJ5AAAwAAAAA8AQCYAAAAACAJxAAAwAAAAA8gQAYAAAAAOAJBMAAAAAAAE8gAAYAAAAAeAIBMAAAAADAEwiAAQAAAACeQAAMAAAAAPAEAmAAAAAAgCcQAAMAAAAAPCHV6h0ArBQIhmRN8T4pqwpKTrpfBuRmSarfx5sCAAAAuBABMDxpV3m1zF1aJAXLik0QnJIiUlMjJvidMCxXJg7Pk47ZaVbvJgAAAIA4IgCG52zYWSFTClZLWXWNVAdDB94YEJm1ZIcULi+WGRMGSu9OmVbtJgAAAIA4Yw0wPJf51eC3tCLYMPjdT6/X26fMW2O21wzxyu3lsvjrb82l/hsAAACA85ABhqdo2bNmfpsLYfX2vZUBueWF9bJ+ZyVl0gAAAIALkAGGZ2jmVtf8Npb5bbB9jciyreWytzIoFYEaKa+qMZf6by2TnjRzpSmnBgAAAOAMBMDwDO32HK/y5YPLpBEbysoBAABgBUqg4Rk66ki7PcdLaP9jFiwtkqtGdY/fA7sY3bcBAABgJTLA8Ayd86ujjuJJM8HhUUpompaLa9m4lo9TVg4AAAArEADDMwbkZpk5v/GmQbCWVyO+3bedgnJuAADgRAGPTjqhBBqeocHvhGG5JgMZbSOsaKT4akuhEZ/u204pK6ecGwAAONGu8mrz2SxcxahLBLVKMvxZeeLwPOmYnSZuRQYYnqK/0FoKHc88cE2otrwa8em+7YSycsq5AQCAE21gSRoBMLxFz2bNGD9A2melSlqcyqH1cbS8GvHrvm3nsnI3l3MDAAD34jNMLTLA8JzenTJl1qTBcsmILtImwy+ZqSmSnZZiLttm+GVol9iC2e8N6ZSQtcVe7r5t57LylpRzAwAAWI3PMLVYAwzPZoJ1jenkkfkm06iBipYxayZ3xnub5Ysd0WcfCX3j333brmXlLS3n1uOMkyTepMfM8s2lsrcyINXl5QlrxgcAQHP/P+IzTC0CYHiafhAd0jX7gD8OC1fujOkxdPtrRh/Ch9pG1H3gDzi/rLw15dz1jzN4q8FIsCYk/hSfOXa80mAEAGAvfIb5H0qgARevV7WD8Af+aNdcp+3f3o5ZMreVcyM5DUb2VdfUzb7WS71eb9ftAABIBj7D/A8BMFAPfxys7b6tt+t2E4bn2fK4dFM5NxKDBiMAADviM8z/EAAD9fDHwbru23q93q7b2bU0tCXrN+1azo3EoMEIAMCO+AzzPwTAQD38cbCu+7Zer7frdnblpnJuxJ8bZ14DANyBzzD/QxMsoP4vxP6ARdfoRfMhlgAnft23nRIkajl34YoSKd0XaHIUkt3LuRF/NBgBANgZn2FqkQEGXLpe1Qndt4/u0dZcOiX4dVM5N+LPyT0ENAu9cnu5LP76W3NJVhoA3IfPMLXIAAON/HGYMm+N+WAaKROsAY4GvwQ43i7nLlhaJHP3l7BqIKMNr8JVAXpihODXW5zYQ6D+uCZzHKeI+RkY1wQA7tSbzzDiC4VCLD5yiK1bt0qy3q68vNqsZlFRkXiVfjAkwLE3OxynGjQ4tZwb8T8Wxvx9uRl1FC1d//7ClUdYcszoGKYpBaulrLqm8RN9aSkyY8JAW6/Nh3v+ngLN4Tj1xmcYn88n+fn5CXt8z2WA169fL8uWLZO1a9ear507d5rr58yZ06LHW716tRQWFsqqVaukoqJCcnNz5bjjjpNx48ZJZiYfGLywXtWufzyQ3HJuwEk9BOqPa2psT/VnKA0GTTWMVjxQ0QAA7pLq0c8wnguACwoKZPHixXF5rHfeeUceeughqampkT59+pizUhpgL1iwQJYsWSJ33nmnZGd776Dyyh+HutLBpcVSFawRn09EE/Tp/hSZMDzXrCXmAyPgLU5pMNKScU16QhAAAKfzXAA8cOBA6dWrl/Tr10/69+8vU6dOlerq6pgfp6SkRP72t7+Z4Pfqq6+W0047zVwfCARMUPzee+/JzJkz5Sc/+UkCfgpYTUsHry5YbUoddQ1ffVXBoDy1eLvMX14sf6N0EPAUJ/QQaOm4Jq2GoboFTR1XVEMBcALPBcBjx46Ny+P897//NYHzsGHD6oJflZqaKldccYXJAL/55pvywx/+UNq2bRuX54Q9aOb3p3NXy7dNrPPToHhPRdBsN/vSIWSCAQ83GAnWiGkuFQyGbNEkjXFNiCcaqQFwGs8FwPGipc5q6NChDW5r06aN9OzZ06wL1kD45JNPtmAPkShPLd7WZPBbn2739OLt8rOTDuUNATzaQ6A4mCF7KwNSXb7XFj0CnDyuCfbSZCO1gJj18IXLi2mkBsBWmAPcQpWVlXXBbiThrO/GjRtb+hSwaYnXguUlMd1HS6GZqQl4kwa7RxzSXkb17WybmddOHNcE+6nfSK2xcnrTSK2itpGabg8AdkAA3ELt2rVrclzAjh07mrwdzvTljnKpinLdXJhur/drigbIK7eXy+KvvzWXBMwAEqUlWWgt3db7Aa1ppAYAdkAJdAsNGTJE3n33XdPs6sILLzRrf8PWrVsnmzZtMt/raKRo3XDDDQ2uS09Pl3vuucd8ryOWkiX884TnraHW1q/2teil2FqRIqdGeC1LyqrkqQ82ysxFX0t1sEb8KT4J1ug6wRS55NgectmoXtI5J52Xn+MUDmbHv6eXHddT/v7uRtPBvjna2f6y43pJfrcuSdk32P841f9fzV++PKZGavOWl8hN3zvC/P8NSMZxCjSGv0ItdOKJJ0rnzp2luLhY/vCHP5iAd9++fbJ06VL505/+JH6/v26QM9wjFMf7rd2xV875y3vmQ+ieioDsq64xXaX1Uv+t1+vt64rKWrnXAHAgPbnWJtNvxrc1RW/X7S4d1ZOXEHW+3PatCYJjoUGw3g8ArEYGuIUyMzPlV7/6lQl+NejVr7Bu3brJ97//ffnXv/4lOTk5UT/m/fff3+TtGmyHdNBsEoTPrFHCfaD8zJoW36/+a6lroSbNXGnWRjX2jmpmprq8Ri56bJHpKMtMYY5TOJNd/57+dVz/qMY16XY15aVS1PRKDnjoOP1m+7fNnjw5mE9C8s32EumWXtXSXQRs+/cU8aUJxPz8fEkUAuBW6N27t0yfPl0++OAD0xVaZwL37dtXjj/+eCksLDTb9OjRI17vFWxgSJdsSUsRqY4hDk73+8z9Wrt2SjvKAkCixjVp7wHt9qwNr+wwrgn2RSM1AE5GANxKGRkZcsopp5iv+nQEUmNjkuBc2jhm/LA8mf1Z9Gcexx+Re0DDGf2QWbCsOKa1U7q9jlOxQwdZAO4c16TzgfWEmwY3dhjXBPuqOz4C0d+HRmoA7II1wAmgo4+++OILk/0dPHhwIp4CFrr06K7SNiO6cSC63SVHdz3gOv2QGWuXZw2C9X4AkAgazOiYpqN7tLXNuCbYV+r+CgENaqMRrijguAJgBwTAzXj55Zdl2rRp8swzzzS4bcOGDRIMBg+47ptvvjFNsNTkyZPj+V7BRhmTRyYOlHaZflMuGIler7frdgeXD2qGJSXG3zx9PL0fAAB2MHF4nqkWaC4E1tt1Oy2nBwA78FwJ9JIlS2TevHl1/w4Eaut3br311rrrxo8fLyNGjDDf79mzR7Zs2SK7du1q8Fj//Oc/TcDbq1cvMxe4pKREVq9ebRZuX3XVVXL44Ycn5WeCNWvnnr1kiFk7N2dpkcnQ6v/kQ/vX/OoHg8bWzrF2CgDgdPr/txnjB0TVSE23Yy05ALvwXACsAe2aNWsaXF//Ot0m2lFI77zzjil5LisrM0GwNsD6wQ9+YBpkwd1aunaOtVMAADegkRoAJ/KFkjVXB622detWxiC5xKMfbJFZS3ZE1QhLz6BfMqILXaAjYBwCnIDjFF44TrW3BY3UkGj8PfUGH2OQAPfREunCFSVSui/Q5Cgk1k4BAJzUSA0A7I4mWICFa6faZ6U22kVTr9fbWTsFAAAAxIfn1gADdsHaKQAAACC5CIDhaVavWWppIy0AAAAAsSMAhiftKq+WuUuLpGBZsQmCdS5vTU3tGqYJw3LNGt1kjmxg7RQAAACQeATA8JwNOytkSsFqKauuadiFOSCmO3Ph8mKZMWGgKVMGAAAA4A40wYLnMr8a/JZWBBsdQaTX6+1T5q0x2wMAAABwBwJgeIqWPWvmt7npu3q7rsctWNqyeYgAAAAA7IcAGJ6ha311zW9jmd+D6XbhNcLJ2LeV28tl8dffmstkPCcAAADgNawBhmdol+VYA0sNgvV+Q7pme6IZFwAAAOBmBMDwDC1p1gAzFim+2vslAs24AAAAgOSiBBqeofN1Nbsai5pQ7f3ijWZcAAAAQPIRAMMzBuRmmdLiWKT5feZ+8UYzLgAAAPehr4v9UQINzwivq9U5v9E0wkrbv32sQXOimnFNHpkf930BAABA69HXxTnIAMNTtKmUljQ3F0bq7brdhOF5tmrGBQAAAHvRvi6TZq40SZa9lUGpCNRIeVWNudR/6/V6u24H6xEAw1O0o/KM8QOkfVaqyfBGotfr7bpdIjow260ZF+BFlKgBAOKBvi7OQwk0PKd3p0yZNWmwFCwtkrnh8UO+2oZX4bJnzfyGg9/A/uyrBqCaFW7JWmK7NuMCvIYSNQCA1X1drhrVnTfBQgTAcKzWBKYa3OofH11X29hjJOqDct1zBKxvxgV4CaPHAADxRF8XZyIAhuPEMzDV+wzpmp3UD8p2acYFeLVErbHfOv19LA0GZcq8NaZKJBFLIAAA7tGavi6RPn8iOVgDDEdJRpOBZKzlsEMzLsBLGD0GAIg3+ro4EwEwHCNZTQaS8UHZDs24AK9oaYlarGf1AQDeQl8XZyIAhmMkIzBN5gflcDOuS0Z0kTYZfslMTZHstBRz2TbDb67X22MtsQZwIEaPAQASoSWNUenrYj3WAMMRktVkINlrOaJpxgWgdShRAwAkAn1dnIkMMBwhWRkcqz4oh5txHd2jrbkk+AXihxI1AECi0NfFeQiA4QjJCkz5oAy4DyVqAIBEoa+L8xAAwxGSFZja+YOyZsBXbi+XxV9/ay5p0APEVqLWWMO5gzF6DAAQC/q6OAtrgOEIdYFpILGBqR3XcsRz7jHgVfp7UriiREr3BZpspMfoMQBAS9DXxTnIAMMRkpnBsdNajmTMPQa8gBI1AEAy0NfF/giA4RjJCkzt8kE5WXOPAa+gRA0AAFACDccIB6Ya7Glzq0hBoQamGvy2NjANf1DWWcJzw6XHvtp1xeHs8oQElx63ZO6xjlRqiv4cjFuCl1GiBgBoDJ+TvIEAGI6SzMDUyg/K8Z57zDpiIHKJGgAAfE7yFgJgOE6yA1MrPii3Zu7xwfuq64O1lFqzyQ0C6oCYdcSFy4tlxoSB5gQDAACAV/A5yXtYAwzHskuTgUSMJ4rX3GPWEQMAAETG5yRvIgMM2LBcJl5zjxOxjhgAAMAN+JzkTWSAARuOJ2pJOffBc49buo44HhlsAAAAO+NzkncRAAM2LJeJx9zj1qwjBgAAcDM+J3kXATCQhHIZK+Yex2sdMQAAgNvwOcm7CIABm5bLhOcet89KbTQTrNfr7ZHmHsdrHTEAAIDb8DnJu2iCBU+LdeB5PMcTJXrucd3PEmj5OmIAAAA34nOSdxEAw5Na2sHZinKZls49Dv8s2pArmox1pHXEAAAAbsTnJO8iAIZrsrPJGHhuZblMeO5xLDSQL1xRIqX7Ak2uWW5sHTEAAIBb8TnJmwiA4an5uvU7ODcWEJoOzsHaDs5aflz/uZxWLhNeR6w/i55IiJQJ1v3T4DfSOmIAAAC34nOSN9EEC56ar9vaDs7xGE+UbOF1xJeM6CJtMvySmZoi2Wkp5rJtht9cr7cfnO0GAABwOz4neY8vFArF3p4Wlti6dask6+3Ky6sthS0qatkIn5ZmZzW4bSo7qzSU1M7HB2dnm6PZ5DF/X24C6WhpgPjClUccEMCa/Zz1ZVRlxS3Zz0SWjCeqrNwqVhynQKw4TuEEHKdwgkQfp277nORUPp9P8vPzE/b4lEDDNlqSndXmUNGKVwdnu5XLxFIy3pJ1xAAAAF7A5yRvIACGo+framfkaM/MxbODc2vGE8VTaxp6OZ2+5ss3l8reyoBUl5dzlhYAAAD2D4A3btwoS5cuNaUMVVVVcs0119TdFggEZM+ePSYN3rFjR0v3E4mVjPm68e7gHMt4okSU1LS2oZdT1c94B2tC4k/xmdc3Hk3SWoJyKQAAAOewLAAuLy+XGTNmyMcff3zA9fUD4GAwKDfddJOUlZXJfffdJz169LBgT5EMyZivm6gOzk2VyySyo3WiS8btyE4Z70S+twAAAHBRF2jN7P7f//2fCX4zMjJkxIgRkpbW8IOi3nbqqaeaxk8ffvihFbuKJEnGfN1kd3BOZEfrlpaMx5plt5P6Ge/Gfm6T8a6ozXjr9k7tVg7v0N/JldvLZfHX35pLJ/+OAgDgBJYEwG+88YasWbNGunbtKtOnT5df/epXkp0dOYN27LHHmsuVK1cmeS+RTC0pCW7JfF3NymnQ3Nwz6e26na7jtWOw1pqScadq7QgrNwbicC49Lh79YIvpTD913hq5+YX15lL/rddz3MBOOFEDwE0sKYF+7733zOXll18unTp1anLbPn36mDXAmzdvTtLewQrh7KxmzqLJarY0O5usDs6JLk9ORsm415qkRcuLpedwbyk/0BSWegBwI0sywJs2bTJB7fDhw5vdNjU11WSH9+7dm5R9g3WSlZ1N9MDzZJQnJ6Nk3E7skvH2Yuk54osKAjgFSz0AuJUlGWDt9pyVlWWC22hoM6yUWNNdcJxkzteNpYOzHTtaJ6qhl13ZJeOdjPcW7kYFAZzAq1MGAHiDJVFl27ZtTRfoiormG8Ts2LHDbNdcqTTcIdHZ2cY6OB/do625jEe5bDKCtWQ39LKaXTLedgnE4UxUEMAp7NJzAQBckwHu37+/6QC9ZMkSOf7445vc9qWXXjKXgwcPjstzr1+/XpYtWyZr1641Xzt37jTXz5kzp0WPp4/1wgsvmMfSoF4z23379pWzzjpLRo4cGZd99ppEZmfdFKxpyXjhihIp3Rdo8kNKa0vG7cAuGW+7BOJwJioI4AR26rkAAK4JgE877TQTAD/33HMmsG0su/vaa6/Jiy++aL4/44wz4vLcBQUFsnjx4rg8lga+Tz75pFnPPHDgQOncubOUlJTI8uXLTWA8btw4+eEPfxiX5/Kipubr2lmygrVklox7pUmaUwJxOBMVBHACTtQAcDtLAmCd+6vjjRYtWiS33HKLjB492qwLVq+//roUFRWZ7LA2y1Knn366DBgwIC7PrYFqr169pF+/fiYTPXXqVKmujn1MyZ49e+SZZ54Rv98vt912mwwdOrTuti+++ELuvvtuKSwsNMG+jnuCd7QkWDv/8M4tynaHS8a1/Gzu/mZLWnKrWcdwEKiZXycHv3bKeNslEIczUUEAJ+BEDQC3syQAVtdee62kpaXJu+++azKpYY899tgB25166qlyxRVXxO15x44dG5fH0TnGGjhrJ+v6wa/Sf+v1mmlet24dAbAHRRusKV1SWrC8WJ5bWmzWl2qJbTjQmhhF8Or0knGnZbztEIjDmagggBNwogaA21kWAKenp8t1110nZ555pvznP/+R1atXm/W4NTU10qFDBxk0aJApez44uLQLDd6jbfgF74kmWEtNEQnWiOhNlVU1rZ4F6tSS8VgcnPHW109PGgSDoaRlvO0SiMN5qCCAE3CiBoDbWRYAh+ka4Hg1uEomLZ/OycmRFStWmJLng0ugly5dKvn5+TJkyBBL9xPWaao82Z8iEqgJSbAmZC7dNGJCf85EZqLrZ7yLgxmytzIg1eV7k5rx9lLpOeKLCgLYHSdqALidLxQKxTbU0mUmTZpkSplb0gVa1zD/5S9/kUAgcEATLM1mawZby7y7dOkSt33dunWrJOvtysurLdvU9diIf1D41tpd8uxnRVGvI9XxTxr02X1upI7OKAgHhC0o53bicZrogB/Od/BxumFnRdQVBPEa+QbEcpzq3/NJs76MaqlH+6xUR52khbPZ4f/7SDxtMKyJRNcFwDoyKCUlRTIzm/6fu84A1rLo7Oxs2wXASjPAf/7zn+Xbb7+tu05HIZ177rlmvXFqavRJ9htuuCFiqfg999xjvm9Js66WCu+3BveIr+pgjRx3z39lT0X0r227zFT58OZTJE1Txza0dsdeufgfH0tZZVCqtC75IOn+FMnJ8MuzV46Ufnk5cXtejlM4QaTjtKSsSp7+YJM8vWiTCYLDJ4w0+L302J5y6aie0jkn3cK9hteP03VFZXLxPz6SvRWN/11vk+mXZ66I7991IJbjFO6VFuVyU8eUQGvm9P777zczgH/+8583ua1mWD/55BO56aab5OijjxY7ef7552XmzJlyzDHHyAUXXGCyvTt27DDjnTSg1tnAN998s9W7CZv5ctu3JgiOhX5A1vsdcUh7sRv9IK/B7+591dLY6TT98FS9r8Z8mHrxutF8sIfnaXA77Yz+MvXUvuZ3W0v522SkyuBubW17ogveokGt/r3mRA0At7EkAP7ggw/MpY4Iao42wtIA+P3337dVAPz555/L008/LX369DGZW81mq549e8qNN95oAl8d5fTpp5/Kd77znageU08KNKW4uJgSaBf4Zvu34ouxQtYnIflme4l0S68dF2Ynj36wxXx4b66WRG//tiIgj/xnZdzKuSmFghM0d5x200SvSfZWye6dJcndOaCZ43TS8PZy4eGHR1zqUVNeKkXlvIRIHv6/7w2+BJdAW3KaecOGDSZg1HWyzTn88MPNi/DVV1+Jnbz99tvmcuTIkXXBb5j+W69XK1eutGT/YF9WjpjQ9aort5fL4q+/NZf679Y+nq75jWYts9LtwmuEAQDOEJ4ycHSPtuaSPgcAnMySDLCOO9I1vbq+tTm6jXZb1vvYSXh/GlubHL5+7969Sd0v2J8VIyYS1aBKMwKxBrMaBOv93D6yCQAAAPZjSQZYM7pVVdGXcuq22gjLTtq3r12LuW7duoi3h6+PZxdo2FOsWdVw0KlBbTTCY3VaesZdO85OmrnSzBXeWxmUikCNlFfVmEv9t16vt+t2sdJyuIMKIJql44L0fgAAAIAnAmAdF6RB7aZNm6Iql9ZtO3XqJFZ4+eWXZdq0afLMM88ccH24xPndd981a5Tr+/jjj831GuiHt4P7aFZV17+O+ftymTpvjdz8wnpzqf/W6/X2xmjGVUuamwtp9XbdTmfKtnQfpxSsltKKyONW6uYNV9TOG25qn+1Wzg0AAAA4ogR66NChsnnzZtMp+Re/+EWT286dO9dcHnbYYXF5bm1MNW/evLp/h9uo33rrrXXXjR8/XkaMGGG+37Nnj2zZskV27dp1wONo5+fjjjtOPvzwQ/nDH/4g/fr1MwvztXlEOPt70UUXSffu9p7dipYxczwLVktZdU3DwDIgJqtauLxYZkwYGHGOp5Yb64zPaGeBtnS+opY96z42V6Sst+t+FCwtiqlBlRXl3AAAAICjMsDnnHOOyY5qplTHHO3evbvBNnrdAw88IIsXLzZNpcaMGROX59aAds2aNXVf4THI9a/TbZqj+3/99dfL1VdfLUOGDJFt27aZn0fHIGnX51//+tdy/vnnixuFAgEJrFolgSWfmkv9t5fEK6uqgfGsSYPlkhFdpE2GXzJTUyQ7LcVcts3wm+v19kgBtF0aVCW7nBsAAABoDV8oHAEmWWFhoTz77LN1Q6379u0rubm5deN+NIsaDNauE/zhD38oY8eOFa/bunWrpWOQanbvlsr586Wq8F+aOpf/dVJKlfSx50nG+edLSocO4nZa3qwZ3mgCSw34NJBtLquqQWekEROtoeuRtSRb1/pGS4Pvh8YPiKlBlQb4k2Z9KaX7Ak1mmvWnaZ+VaoL6lma0D8Y4hPhKxHEIjlM4A39P4QQcp97gS/AYJEtKoJUGtNopedasWVJRUSGrV682X/VlZWXJJZdcYmYBw1rBjRulbNr1EiovF6lumNGsem6OVD+/UHIemC7+nj3FrVqaVZ08Mr/JQCI8YiKektWgKlnl3EicRHUJBwAA9hXw6IlvywJgddZZZ8no0aPNOtpVq1ZJaWmpub5Dhw5mRrCusW1szBCSRzO/JvjV0vDGMtDV1eb2smnTpM3jj7s2E+yksT/JbFAVLufWNcRzw0GUr/bxwmXP2siLIMp969kBAICz7PL4iW9LA2ClM35PP/108wV70rJnk/ltrvw6FJJQWblULVggmZMnixvPjm3aVSE+X8gRY3+S3aBK/1Bqqbdmu714NtHp69kbO6rNevZg7Xr2eJavAwCA5NvAiW/rA2DYmza4Mmt+I5Q9R1RdLZULCiXj0kvFl5rq6NKPSGfH9LIqxgywVWN/wmfxYlmvHI8GVYko50ZiJLpLOAAAsA9OfNeyf4QCSwV1pFOsXZ4DAXO/1EGDxKmlH8f2bCu3vPBV5LLQGFk59kdLWApXlETVoKo184bhPIlazw4AAOyJE982CIB1dNAHH3wgG7XBUllZ3UzexrqB3X777UndP2jap7y223MsdHu9n0NLP2Z+sl2e/Hi7+Wdre25bPfaHBlVww3p2AADQOpz4tkEAPHfuXJk/f77UxNqlB8mVk12bGo2Fbq/3s2GJc25OarNrHmOYGtQku2RVaVAFK7uEAwAA63Hi2+IA+J133pGCggLzfceOHWX48OHm0u9P/jpJNM3fr5+Z8xuT1NTa+9mwxLkmFDJZrERPU7bb2B8aVMHKLuEAAMBanPi2OAB+5ZVXzOVRRx0lN9xwg6Q6oFmSV2kjq/Sx55k5v1E1wkpLk4xxYy1tgNVkiXOCpPt9kpris/3YHxpUwaou4QAAwDqc+P4fS6KUr7/+2lxeeeWVBL8OkHH++VK9cKGESpuYA6x8PvHlZEv6uHGWDeeOprtdIkw94RDp0ymTsT9wDKu6hAMAgOTjxPf/WJamy87Olk6dOln19IhBSocOkjN9upRNm2bm/EbMBKelmeBXt9PtrRrOHW13u3hrk+6Xo3u0TfKzAq1Dl3AAALyBE9//E2MLlPg45JBDpLKyUqqjnS0Ly/l79pQ2jz8uGRddKNKmjUhmpp7FqL1s08Zcr7frdq0tX540c6XJSu2tDEpFoEbKq2rMpf5br9fbdbvWdreLp07ZlPHDecJdwttnpZoMbyR6vd5ul/XsAACg5Se+c9L9plGrExq5Jooln9pPO+00eeyxx8wIpJNOOsmKXUALaGY3c/Jkybj00tr5wJoNzsk2Da/isea3tcO5W9LdLh40bmiXSQAMZ6JLOAAA3sB4zFqWfGo/44wzZMmSJfLEE09Ibm6uDB061IrdQAtpsJs6aJDthnO3pLtdPGSmpdAYCI5Gl3AAALyhd6dMk0TSz9Fzw8sNfWL7Rq6OD4B1BFLv3r3lyy+/lDvvvFMGDx4s/fv3l6yspruLTpgwIWn7COcN525Jd7vWSk0RuWB4Ho2B4Ap0CQcAwP06ZqeZJJJ+jo614awbWBIAz50794B/ayCsX80hAHaveAznbkl3u9Zqk5Hq2vURAAAAcK9Uv6/uc7SXWBIADxkyRHw+959dQHKHc8fa3S7a59CSkINpiYieKaMxEAAAAOAclgTAv/nNb6x4WnhgOHe0Y12ao6dn2mX6ZczgTvL8yp2eXB8BAAAAuA2ta+Gq4dzRdLdLTfFJsCYk/hSRQE3T2V1tFHDN6EMSvj5CA2wvrsEAAAAAkokAGK4bzh1Nd7szB3aU11bviqr7XXPrIyqqauTNdbtl175q6ZiVJqf26yCZ6SlRj37S7tcF4f1IEZMJD78emtEmywwAAADEhy8UCiV/cCpaZOvWrZKstysvr7axU1FRkSSLBoOTZn3ZbPmyhrzts1IbzAFuSWa1NZnXDSX75J43NsmyreUNbhuWny03n9ZTenduvLP5hp0VZu6xjn6KFPSbTHRaisyYMNAE9WSJ7XGcArHiOIUTcJzCCThOvcHn80l+fr47A+Dq6mpZtGiR6QBdUlIilZWVjQZ4+kLcfvvt4mVuD4DrgsImypcPLk+2yntflcovn1/fbKB+77l9ZXSf9pGD/ZkrpbQi2Oxj6FrkcwZ3koXhtchkievwP0I4AccpnIDjFE7AceoNvgQHwJaVQK9atUqmT58uO3futGoXYENOGM6tmd/mgl+lt+t2+vMc2iHzgEzzW2t3mcxvNI+hQfKcpUUNu1EHxJSMFy4vrssSAwAAALBZAFxcXCz33HOPlJeXS8+ePeXII4+Uf//735KZmSljxoyR3bt3y4oVK2THjh3Stm1bOfPMMyUl1hk5cCy7D+fWsudo8/C63bTCtVIeCNXL3oakIhBbJj/SKCalWfLSYNBkzaMpCQcAAAC8zJIAeOHChSb41cD35ptvNmnucAB84YUX1m336quvyhNPPCEbN26UX/7yl1bsKixkx+Hc2vAq0prfpuwoi6G1dQtobKwnCTRrricOAAAAAERmSVp12bJl5vKCCy4wwW9jzjrrLJk4caJ88skn8vrrrydxD4HItNuzHWkmONxJGgAAAICNAmAtgdaS5j59+hxwfSDQMFN29tlnmyD5rbfeSuIeApHpqCO70iBYS8YBAAAA2KgEWjsZ5+TkHLCuNyMjw5RF6231s8LZ2dnma/PmzVbsKnAAnfNrV9osTEuhAQAAnIRRj3B9ANypUyeTBa6vc+fOsmXLFhPoHnrooXXX62iksrIySU21rGE1UOfUfh3krtc22vIV0UZZ2iwMAADACXQs5NylRXXLuBj1CNeWQHft2tWUO2/btq3uuv79+9c1vjq4YZbq0qVLkvcSaCgzPUWG5durMVeYjonSTtkAAAB2t2FnhUyaudKMdNxbGZSKQI2UV9WYS/23Xq+363ZAPFmSVh06dKgsXbrUNMPq1q2bue7000+Xt99+W1555RXZvn279O7d23R//vTTT83to0ePtmJXgQZuPq2nTJr1ZdSjkFoqPP84GuEZyXYYEwUAANBc5ndKwWoprQg2+nmKUY9wVQb4+OOPN0Hw1q1b664bPHiwnHvuueb7zz77TAoLC+uC3yFDhsjYsWOt2FWggd6ds+Tec/tKvEPNtBSRzFSfpPt90jbDLxcMz5N2mf5mn0dv19LnCcPzeLcAAIDtadlzWXVNs8mE+qMegXjxhbTrlI1oVvj999+XkpISycrKMrOCTz75ZPH7WduoJwyS9Xbl5dUGU0VF/MFpzIaSffKHN76WpVvLGtzWJSdVdu4LSiCKFK5mejVxq83ftP+bvsWa0Z04PE+O69VObn7hK/PHX8+EHky30+B3xvgB0rtTpngNxymc0JSF4xROwHGKZP5tHfP35abMOVqaGHjhyiMkv1vtkkg+n7qbz+eT/Pz8hD2+7TpLDRs2zHwBTsgEPzxxoFRU1Zj5wDoiSbtEa6OsfYGgKZMu3Rdo9uymxsi1cfL/tqwKhszal8LlxXLP9/vKoo17ZG64QcT+0uhw2bNmfjtm27c7tZPRldKbaMoCAImjJxb1/68tGfWYX7tyEnBXAAw4sTHWOUM6NbhOs7JT5q1pNHvbnPDaF80Az5o0WCaPzI9LNgrNIwDyLm22ouvStDSvwe9tQOpOTM2YMNCTVRcA0Fr6OabeJNSoMOoRjl8DDHiBfjjWwPWSEV2kTYZfMlNTJDstxVzqOl/9Yy4xrH3RYHdI12w5ukdbc0nwmxh0pfSu+k1ZGjtpZU5MVQTNyS3dHgAQGz2JX1MT230Y9QhHZYDfeustc5mdnS3HHHPMAdfFStcCA06ipclXjep+QPZWA+Dr/7XWlDlHQz9w63w8fQyC3sSiK6W3taQpi/5+AwCiV1fBFoj+Pox6hKMC4BkzZpjL7t271wXA4etiXQxNAAynCmdv1crt5RKM8cxneO1L+DGQGARA3hXYf6Ip2uUKnJgCgJZ/JtIeJrqkJJq/uYx6hOMC4NzcXHPZqVOnBtcBXsTaF3siAPK21jRl4cQUAMRGJ10Urihptlkoox7hyAD4oYceiuo6wCtY+2JPBEDexokpAEjuErHmmoXWH/XItAvEE12ggSRj7Ys9EQB5GyemAMCaZqHaT4FRj3B9ABxeAzxhwgTp0qV2oDXglTmurH2xJwIgb+PEFADYo1moXT6vwb0sCYDffvtt8fv9cs0111jx9PAAu89xZe2L/RAAeRsnpgDA2r/B9FOAq+cAt2/fXtLT001nZ8CLc1zDa1/aZ6WaNS6R6PV6O2tfkhsANfZ+HIyulO6jJ6Y089DcEUBTFgAAnMuSALhfv35SXl4uO3futOLp4ZE5ro211tfr9XZtvKDbW7325ZIRXaRNht/MB85OSzGXbTP85nq9XbdDchAAeRsnpgAAcD9LSqDHjBkjn3zyicyZM0euvvpqK3YBLuW0Oa6sfbEXulKCpiwAALibLxQKxTb4ME5efPFFefrpp+WYY46RsWPHSt++fa3YDUfZunWrJOvtysvLM5dFRUXiFLrWd8zfl5sy52hppvWFK49ostGCHRtpIbHHqVYG0JUS8frdd+LfU3gPxymcgOPUG3w+n+Tn57srA3zttdeaS22EtWjRIvOla4Lbtm0rKdqtqJEX4sEHH0zynsLLc1zt3kgLiUNmHgAAwJ0sCYAjnQWvqqqSkpISK3YHLsm+xHOOqzbI0rXEWk7dYC1xQEwjrcLlxTJjwkA5tH0GGWKXoiulN3HyCwAA97IkAGb8ERLx4TNec1zrN9JqLJ9sGmkFg/Lj2V+KP8UnwRohQ+xClL97Tywnv2hQBwCA81gSAJ9yyilWPC1c/uEzXnNcY2mkVRHQ/9rjQzLBWvyQAfSmWE5+aRd57dLOMggAAJzFkgAYSMSHz3CGWIPPxkYgNTfHVYNIzTxHc/+mJPNDMsGavTOAnJhwDqd1kQcAALEjAIarPnxqeXThihIp3Rdo8nE05NXS5wnDa7uztqaRVkv2M15BklvLNa0KGuOZAeTEhLPEevKrev/2k0fm0xUeAAAHsUUArKN9ysrKpKKiosntcnNzk7ZPcOaHz9bOcW1JI62W7Gc8giQ3lmtaHTTGKwPo1hMTbhbvLvIAAMCeLA2AP/nkE3nppZdk9erVUllZ2eS2OgZp9uzZSds3OPfDpwYUGuy1ZI5rSxpptXQ/Wxskua1c0+qgMV4nYdx4YsIL4tlFHgAA2JdlAfDMmTPl+eefjylLHA/r16+XZcuWydq1a83Xzp07zfVz5syJ6XH++9//yowZM5rdburUqXLyySe3eH+9It4fPls6x7UljbRasp+tDZLcVq5ph6AxXidh3HZiwivi1UUeAADYmyUB8GeffWaCX7/fLxdffLEceeSRcuONN0q7du3kd7/7nezevdsEqS+//LLJ/E6ZMkV69OgRl+cuKCiQxYsXt/pxunXr1mhgW15eLh9//LH5fvDgwa1+Li9I1IfPWOe4xtpIq6X72dogyW3lmnYIGuNxEsZtJya8JF5d5AEAgL1ZEgC/9tpr5nL8+PHy/e9/v+76lJQU6dq1q/kaNGiQnHbaaXLnnXfKww8/LPfdd19cnnvgwIHSq1cv6devn/Tv399kaKurq2N+HA1sGwtuX331VRMA68+gPwuc9eEz2kZaLd3PeARJbirXbM3rYbeTMG47MeEl8egiDwAA7C+O7X6ip6XH6vTTT2+yzLlz587y4x//WPbs2SOFhYVxee6xY8fKhRdeKEcffbR06NBBEuGdd94xlyeddFJCHt/NHz71Q2U0EvnhM9xIq31WatT7E8t+tiZIcmO5Zjxej3hoSafpg09uuOnEhBfpyS/9HWnuKGisizwAALA/SwLgvXv3SkZGxgEBqGZ/IzXCGjZsmKSlpcmnn34qTrBjxw5ZtWqVpKamyqhRo6zeHUex04fPcCOtS0Z0kTYZfslMTZHstBRz2SY9RTJSmw+UGtvPeARJ8QjW7MIuQWM8TsK46cSEF0Vz8kuv19sjdZEHAAD2Z0kJdFZWlgSDB354zc7ONoGxjkLKzMw8IDDWtcLhZlV29/bbb5vLESNGSJs2bazeHUdp7QijROxPY420vimtbPF+xiNIclO5pp2CxtbOkbZTKT9apjVd5AEAgP1ZEgB36tRJNm3aJFVVVZKenm6uy8/PlzVr1pjs6fDhw+u23bp1a4Og2M4of3bfh89IjbRas5/xCpJaG6zZhZ2CxtaehHHTiQkva2kXeQAAYH+WBMA9e/aUjRs3yoYNG0xTqnCpswbAzz77rGlSpeXRuvb3kUceMbdr0yq707XNGrBr5lczwLG64YYbGlynJwjuuece831ubq4ki5Zwq7y85AdN+oy/7tVdbvpejXy57VvZWxmQNhmpMrhbW0nzW1K1H/f9vOy4nvL3dzdKVbD51Ge6P0UuO66X5Hfr0uD5Z1/VXi7+x0eytyIY8bH0vm0y/fLMFSOlX16O2FVLX49EHKf6UC//vKs8/cEmeXrRJhPIaom2Zqk1YL302J5y6aie0jmn9uTdwa4+vb38+4udZrRTU9PbfD6Rtpmp8tPThzT6WLBefjdn/z0FosVxCifgOIVjA2Ade6SZ0o8++qguAP7ud78rL730knz11VdyzTXXmJFIpaWldY2xfvCDH4hTyp917W/4FxQtp0HkEYe0d+V+Xjaql8xe/I3sKq9pNkjSAFYDrkg0qH3xutEtDtbsIl6vR7zo6zXtjP4y9dS+MZ/c0PvqCYdoT0zY/b0BAABwE1/o4NbLSaClz++//77JlGo35rDVq1fLAw88IMXFxXXXabOsSy+9VM4888yE7MukSZPMGKQ5c+a06nF0TfPVV19tgva77rrLjECKN80uJ+vtCmcqioqKkvJ8XrRhZ4VcU7Bavq0MmtLpg2lJddsMvzw8YaApuW6OlmE7uVxTX49oS4/Dr4edj1PNANuplB/WsfNxCoRxnMIJOE69wefzmeWxiZKUNOW9995rRh595zvfMU2ttKz3lFNOabCdZoMffPBBEwhrEKyNsXTWrl7a3dKlS03wG55hDESjuRMasZzwiLRW2UnsuP47HutILz2qm7y5brfs2lctHbPS5NR+HSQz3T6l/AAAAF6SlAD4k08+MV+6rvfkk082wW/37t0jbqsBsga9ThNufnXiiSdavStwAM0OTilYLXsqG1/zqoGf3q5ZUQ0MnRL4tYabmg/pezx3aZEUhIP5/aXpf377GxPMaxMzL7ynAAAAnguANSu6fft22b17t/zrX/8yX5olPe2008x6WS1zdjLtUr148WLzPQFwrVAgIMF160TKykVyssXfr5/4WBddRwOjvVHOsdX1p5oV1cDQK5yezTbl3AWrpay6pmE5d0BMl+jC5cUyI8rydgAAADgoAP7LX/4iX3zxhbz55puyaNEiqaysNOOO9OuJJ56Q448/Xk499dS6hlh28vLLL5uvkSNHysUXXxxxm/DPNGDAgITWqztBze7dUjl/vlQV/kskEJC6tFdqqqSPPU8yzj9fUjp0EC/TbKAGwIEoZ9/qdnOWFpmsqNOyoF7O7pdWBBsdT6VBcWkw6KnsPgAAgB0krVXx0KFDzdcVV1wh7733nvz3v/81a301e/rGG2+Yr0MOOcRkhU866STTBToRlixZIvPmzav7d0CDNBG59dZb664bP3583RgjHcW0ZcsW2bVrV6OPyezfWsGNG6Vs2vUSKi8Xqa5u8DpVPTdHqp9fKDkPTBd/z8R28bUzLe2tqI4y+t1Pt9f7OTkr6hV6ckMzv82t3tbbtcTba9l9AAAAKyV9Vk9mZqZpiKVfGlhq4KsBpJZHb968WZ5++ml55pln5KijjjJZYW2cpZ3A4kUDWp03fLD61+k20dLAeMWKFeL3+00m28uZXxP86mvXWOOm6mpze9m0adLm8cc9mwneUxGQCE2Om6Tb6/1g/+y+rvmN1MU6kur925PdBwAAcPEYpIPV1NTIZ599ZoJhzdDqSKGwjh07mqZZ+tWtWzfxMjuPQdr3+OMmwxsp89tAWppkXHShZE6eLF700sqdctdrG2O+321n9pJzhnRKyD45ld3GIazcXi5T562Rimjr2/WkYGqKPDR+ANl9F7PbcQpEwnEKJ+A49QafG8YgNUc7P2vJsX5p9vXtt982JdJff/21ybAuWLBACgsLZfbs2VbvKhppeGXW/EYT/KrqaqlcUCgZl17qycZYnbJTk3o/JI+WNOuy91joqCe9HwAAABLPdsMode3v97//fTM7eMKECXXlzzZIVKMRptvz/rXUUQt3ifagdpmpkhrjb55ur/eDvenIJu35Fgsdd6X3AwAAQOLZ7hP1tm3bTCm0ZoHrN55KT0+3dL/QBB11FHPaK6X2fh6kM20zUlMkUBVbmazeD/ZWN684hvNBaX4f7y0AAICXAmAdIfT++++bMUk6Gqm+3r17m2ZYzNe1sZzs2lFHsdDt9X4epAHSxOF5MvOTHRLQ9F9z26fUbs8IJPvT92jCsFwz5zeaRlhp+7fnvQUAAPBAAPzll1+abK/O0dVxSGHZ2dlywgknmJFIffr0sXIXEQV/v35mzm9MUlNr7+dRGtAWriiR0n2BJsfl6AKANhl+mTC8tomOUzoh68gmXdeqpb11WVGPiOW91dfHSe8tAACA0yU9ANayZm1wpV9a7lzfYYcdZoLeY489VtLS0pK9a2ghbWSVPva82LpAjxvryQZYYR2z02TG+AEyZd4aEyhGyhZqdlADJN1Ot7e7XeXVZgaujvXRIFir3DXRH86KamDohJ+jtdz43gIAALhFUiKQQCAgixcvNiXOy5YtM2OPwjp16mRGHGmZc5cuXZKxO0iAjPPPl+qFCyVU2sQcYOXziS8nW9LHjfP8+9C7U6bMmjRYCpYWydxw0OirbYoULo3V7KATAqQNOytkSsFqKauuaRjwBcSUBBcuL5YZEwaan9vt3PTeAgAAuElS5gBfccUVsnfv3rp/+/1+Ofroo022d/jw4XWdnuHcOcAquGmTlE2bJiFtbhUpE5yWZoLfnOnTxd+zZzx31/GcXDasmd9JM1dKaUWw2ZLf9lmpJjCMV+DnhHmATn5vIZ45TgGOUzgBx6k3+NwwBzgc/B566KEm03vSSSeZcUdwFw1q2zz+uFQtWGDm/JrRSHV1sKmm7FkzvykdOli9q7ajAdGQrs5sCqZlz5r5be7UjN6uQaBmRa8a1V28wsnvLQAAgNskJQDWTK9+DRgwIBlPBwtpcJs5ebJkXHpp7ZxfzQbnZJuGV15e8+vWLKD+XLrmN5qOx6p6//aTR+a74ucHAACAsyQlIvnpT3+ajKeBjWiwmzpokHid2xtDaVCvP1csNAjW+5EVBQAAQLJ5NyUHJJgXGkNpRluD+lhoMyi9HwAAAJBsMX50BRBt5leDX20M1Vh5sF6vt+u4HN3eibScu15T96hoJ2S9H4DaZQQrt5fL4q+/NZexVlQAAIDYkAEGEsArjaHq1jIHor+PjgHS+wFe5vblEQAA2BUZYMAmjaGcmPkJf1jXoDYa4Rm4NMCC15dH6OgwXQaxtzIoFYEaKa+qMZf6b71eb9ftAABAfBEAAzZqDOVEmqnSkubmQmC9XbebMLx2JirgRV5ZHgEAgF0RAANx5rXGUFqmOWP8AGmfldpoJliv19t1O8o64WUtWR4BAADihwAYiDMvNobSLtazJg2WS0Z0kTYZfslMTZHstBRz2TbDb67X253a7RqIBy8tjwAAwK5oggXEmVcbQ2lmVxt5TR6Zb8q5NXulQX3d6wF4HHOzAQCwnuUB8MaNG2Xp0qVSVFQkVVVVcs0119TdFggEZM+ePeLz+aRjx46W7icQa2MobWQTTabHbY2h9OcY0jXb6t0AbMdryyMAALAjywLg8vJymTFjhnz88ccHXF8/AA4Gg3LTTTdJWVmZ3HfffdKjRw8L9hRoWWOowhUlUrov0ORaPxpDAd7hxeURAADYjSVrgDWz+3//938m+M3IyJARI0ZIWlrDeYd626mnniqhUEg+/PBDK3YVaBEaQwE4WEuWA7hheQQAAOL1APiNN96QNWvWSNeuXWX69Onyq1/9SrKzI5dMHnvsseZy5cqVSd5LoHVoDAWgPuZmAwDg0RLo9957z1xefvnl0qlTpya37dOnj1kDvHnz5iTtHRA/NIYCUB/LIwAA8GAGeNOmTSaoHT58eLPbpqammuzw3r17k7JvQCIbQx3do625dEvDKwCxYXkEAAAezABrt+esrCwT3EZDm2GlxNo6EwAAGy+PKFhaJHP3z/nVbs/a8CrcFX7C8DwTLAMAABcEwG3btpVdu3ZJRUWFZGZmNrntjh07zHbdunVL2v7BO/SDZzJm1ibreQA4A8sjADSHzw6AiwLg/v37mw7QS5YskeOPP77JbV966SVzOXjw4CTtHbxgV3m1zF1aJAXh7EuKmPEk4SY1E+OUfUnW8wBwJuZmAzgYnx2AxLKkrvi0004zl88995zs3Lmz0e1ee+01efHFF833Z5xxRtL2D/agAePK7eWy+OtvzaX+Ox427KyQSTNXyqwlO2RvZVAqAjVSXlVjLvXfer3erts54XkAAIA78NkBcGkGWOf+6nijRYsWyS233CKjR48264LV66+/LkVFRSY7rM2y1Omnny4DBgywYlfhsjOf+thTClZLaUVQGgunq4MhKQ0GZcq8NWadXkueK1nPAwAA3IHPDkByWNZZ6tprr5UTTjhBdu/eLS+88ILs27fPXP/YY49JYWFhXfB76qmnyhVXXGHVbsJlZz41sC6rrmk0KA3T23W9rjapsfPzAAAAd+CzA+DyADg9PV2uu+46ufPOO+Wkk04yTa70Ou0MnZuba7LCd9xxh1x99dXi9/ut2k1YdOZTs6ONZk0rarOmxXurYyqR1ts1q9zYY0d6rnAWOhbJeh4AAOAOfHYAXF4CXZ82t6LBFWI987mnIiDjn/xc/D5f1CXS2oU51iBTg1O9n87ujVayngcAALgDnx2A5GG4Lhx55lPnZeq2sZRIa6lxrOOkdTan3i8WyXoeAADgDnx2ADwQAJeXl5v5vs3RbXRbuFtLznw2VyKtJdX16fxdzRTHQgNtvV8skvU8AADAHfjsALg8ANbuz5MnT5ZHHnmk2W3/8pe/mG0XL16clH2Dc858xtpYakBulimTjkWa32fuF4tkPQ8AAHAHPjsALg+AP/jggwPmATclPP/3/fffT/h+wVlnPmNtLBVeI6zBZjTS9m8fazCbrOcBAADuwGcHwOUB8IYNGyQlJUUGDRrU7LaHH364+Hw++eqrr5Kyb3DOmc9oG0vVpw2yNNhu7pn0dt1uwvC8Fj13sp4HAAC4A58dABcHwDt37pTs7Gwz9qg5uk1OTo65D9wr1jOfLW0spd2hZ4wfIO2zUht9Lr1eb9ftInWTjkayngcAALgDnx0AFwfAmtGtqqqKenvdtiae9bFw9JnP1jaW6t0pU2ZNGiyXjOgibTL8kpmaItlpKeaybYbfXK+363atkaznAQAA7sBnB8Clc4A7d+4smzdvlk2bNknPnj2bLZfWALhbt25J2z9Ye+ZTOzhr5jbakUgtaSylz3XVqO4yeWS+KZPW59NgOd6l2Ml6HgAA4A58dgBcGAAPHTrUBMBz5syRX/ziF01uO3fuXHN52GGHJWnvYIczn9rBee7+JlZayqzZ3JpQSAI1IfN9vBpL6e1DumY3uY3uQ2uD12ieBwAAgM8OgAsD4HPOOUdef/11+fjjj82Yo8suu0w6dOhwwDa7d++WJ5980ow/0oZZY8aMsWJXYaMzn7k5qXL5s6ukdF/AjDpKdGMpnSM8d2lRXTdpHdOklfjh9cpass3aXThFPE7kAAAAOJ0lAfAhhxwiF110kTz77LPy3nvvmbnAffv2ldzcXHN7cXGxrFu3ToLB2gZGF154oRx66KFW7CosFClr2lyJtGZ+9cN9axtLbdhZIVMKVktZdU3D5wmIzFqyQwqXF8uMCQNZwwtb40QOAADA//hCoVDrFlq2wquvviqzZs2SioqKiLdnZWXJJZdcUjcL2Ou2bt0qyXq78vJqs6dFRUVixw/0kUqkw2XPmvltTfCrjz9p5koprQg2m2nWLs5ask0m2Bp2Pk7toMkTOeETRmkpnMhJMI5TOIETjlMqWeCE4xTxaZicn58vrsoAh5111lkyevRo+fDDD2XVqlVSWlpqrtdyaJ0RfNxxx5lxSUAym0No2bMGDM2datDb9bk1GNf9ccP/+Plw4R56IkeD36ZO5GhQXBoMmqoKTuQAsCsqWQC4JgBWOuP39NNPN1+A1Y2lNADUNb/RdqCu3r+9BuOJWk+ZjP/x8+HCfex6IgcAYsGSJACumAMM2JVmWDXIjIUGwXq/RP2PX8uxdc3x3sqgVARqpLyqxlzqv/V6vV23s/NzILlaeiIn1mMfAJJVydLY3zNTyVJRW8mi2wNAcwiAgXo0E6YZ1ljoGmS9nxP/x+/kDxcarC3fXCofrC+RldvLCd5sfCIHAJJVyQIAti6B3rZtm3zwwQeyceNGKSsrk0Ag0ORi6Ntvvz2p+wfv0bW1Wl4cC23ApfdzYgmrE8tk65drB2tC4k/xmWCP8VT2PJEDAG5ZkgTAHSwLgOfOnSvz58+XmlijDSCB6hpLNX4uJmInXb2f0/7H78QPF6wFc96JHABIdiVLvPuDAHAXSwLgd955RwoKCsz3HTt2lOHDh5tLv58PX7BWOIuo616jCQzDo5fiHRAm43/8TvtwQVdj553IAYCWopIFgKsC4FdeecVcHnXUUXLDDTdIaqrlzaiBOtpVuXBFiZTuCzQ7B1gzZjp32In/43fahwsnlmt7/UQOALQUlSwAXNUE6+uvvzaXV155JcEvbEdHCs0YP0DaZ6WawCASvV5v1+1aO4LIqv/xO+nDBV2NW3YiR9+r5kLaRJ7IAYCWasmseypZAETDstRrdna2dOrUKenPu379elm2bJmsXbvWfO3cudNcP2fOnBY/5o4dO6SwsFCWLl0qu3btkqysLOnWrZsce+yx8oMf/CCOe49k6d0pU2ZNGmyyiHPD83d9tQFgOFumAUMigt9klbA6qUzWaeXadjqRo927NSMeKROs76cGv4k6kQMALUUlCwBXBcCHHHKIbNiwQaqrqyUtLbkfunTt8eLFi+P2eJ9++qncf//9UlVVJX369JEBAwbI3r17ZdOmTfLaa68RADuYBgRaQqtNnzSQ0iBCg4WWnJW24//4nfThwmnl2nZh9YkcAHD6kiQA7mNJAHzaaafJY489ZkYgnXTSSUl97oEDB0qvXr2kX79+0r9/f5k6daoJxFti8+bN8sc//tFkfP/f//t/MmjQoLrbtLv1V199Fcc9h1U04LMii5iM//E75cOFk8q17cbKEzkA0BpUsgBwTQB8xhlnyJIlS+SJJ56Q3NxcGTp0aNKee+zYsXF7rKeeesoEzzfeeOMBwa9KSUkxQTZg5//xO+XDhZPKte3KqhM5ANAaVLIAcEUArGXIvXv3li+//FLuvPNOGTx4sMnGaia1KRMmTBC7KC4uls8++0y6du0qI0aMsHp34FLJ+B+/Ez5cOKlcGwAQX1SyAHB8ADx37twD/q2BsH41x04B8BdffCGhUMiUVAeDQVm0aJGsWrXKlD736NFDjj/+eGnTpo3VuwkXSMb/+J3w4cIp5doAgMSgkgWAYwPgIUOGiM9njw/VLfXNN9+Yy8zMTLn99ttlzZo1B9w+e/ZsM+P48MMPt2gP4TbJ+B+/nT9cOKVcGwAAAPblC2ka08MmTZpk1vHGOgbp0Ucflddff138fr8Jgq+44go58sgjZc+ePTJv3jx55513zKgn7RAd7bgnDZgPlp6eLvfcc4/5vqXNuloiNbX23EggEMOiSyAJSsqq5OkPNsnTizaZINifIhKsqQ1+Lz22p1w6qqd0zknnvYBt8PcUTsBxCifgOPWOtAROCrJsDrDThc8baPnzVVddZUqelZY9X3fddbJlyxZZt26dvPLKK/LDH/7Q4r0F3EOD22ln9Jepp/aVNUXlsrcyIFmpPhncra2kaTQMAAAANIIAuIU06xu+HDVqVIPbTz31VBMA61rhaGm2uLnGW8lK2Ofl1a6fLCoqSsrzAS0xtNv/jtPdO0t4EWFL/D2FE3Ccwgk4Tr3B5/NJfn5+wh6fdEkrfwF1jFOk9czh27UkGgAAAADg8QywrmnV7snaAbqkpEQqKysbzXBqkKnNpuxCxzipsrKyiLfv3bv3gEwxAAAAAMCjAbCODJo+fbrs3LlTnGjQoEHStm1b2b17t1nv27179wNuD5c+hwNlWENn2tp1rA8AAAAADwTAupZVOxuXl5dLz549Tffkf//73yZbOmbMGBNUrlixQnbs2GGCzDPPPFNSUqyp1n755ZfN18iRI+Xiiy+uu167P3/ve98z447+8Y9/yI033mi6Pqtly5bJf//7X5O11n1H8u0qr5a5S4ukYFmxCYL18KmpqR3zM2FYrpkpy5gcAAAAwFssCYAXLlxogl8NfG+++WYTKIYD4AsvvLBuu1dffVWeeOIJ2bhxo/zyl7+My3MvWbLEjCkKC4/5ufXWW+uuGz9+vIwYMaJuDa9meHft2tXgsX7wgx/I559/LsuXL5ef//znMmDAAPn222/NTOCamhq56KKLpH///nHZb0Rvw84KmVKwWsqqaxrOig2IzFqyQwqXF8uMCQOldydK1AEAAACvsCQA1gypuuCCCyI2kAo766yzzFra5557zszcPeOMM1r93BrQaoB6sPrXRdu4SmeR3XLLLfLCCy/I22+/LUuXLjXXDR061GSHjzrqqFbvL2LP/GrwW1oRlMb6ZWtQXBoMypR5a2TWpMFkggEAAACPsKwEWkua+/Tpc8D14WxsfWeffbbMmTNH3nrrrbgEwKeccor5ipYG6frVGA14zzvvPPMF62nZs2Z+mxsWpbfruuCCpUVy1agD128DAAAAcCdLFtZqp+ecnJwD1vVmZGSYsuiDu0Drulr92rx5swV7CifRtb665rdB2XMjdLvwGmEAAAAA7mdJANypUyfZt2/fAdd17tzZrJs9ONDV0Ug6akgvgaZot+dYg1kNgvV+AAAAANzPkgC4a9euptx527ZtddeFm0Vp46uDG2apLl26JHkv4TRa0hxrs/AUX+39AAAAALifJWuAtUmUNozSZljdunUz151++ummkdQrr7wi27dvN/Nztfvzp59+am4fPXq0FbsKB9E5vzrqKBY1odr7AQAAAHA/SzLAxx9/vAmCt27dWnfd4MGD5dxzzzXff/bZZ1JYWFgX/A4ZMkTGjh1rxa7CQQbkZpk5v7FI8/vM/QAAAAC4nyUZYC1nvuOOOxpcf8kll8iwYcPk/fffl5KSEsnKyjKzgk8++WTx+8nSoWka/E4Ylmvm/EbTCCtt//axBs0AAAAAnMmSALgpGgDrF9ASE4fnSeGKEindF2hyFJKGvFr6PGF4Hi80AAAA4BEpVs0B3rlzZ9Tb67Z6H6A5HbPTZMb4AdI+K9VkeCPR6/V23U63BwAAAOANlmSAp06dKh06dJBHHnkkqu1vu+02UxI9e/bshO8bnK93p0yZNWmwFCwtkrn75/xqt2dteBUue9bML8EvAAAA4C22K4FuTCgU23xXeJsGt1eN6i6TR+abOb866khLnlvSKAsAAACAOzgiAK6urqYJFlpEg90hXbN59QAAAABYswY4Frr+d8+ePdK2bVurdwUAAAAA4GBJyQB/8cUX5qu+iooKKSgoaLLkuayszMwC1u/79++fhD0FAACRaD8FlpQAAJwuKQHw559/3iDY1QB47ty5Ud0/LS1Nxo0bl6C9AxKPD44AnGpXebXMXVokBeGmgikiNTX/m72u4+doKggAcIqkBMB5eXkydOjQun9rNjg1NVUGDhzY6H18Pp9kZWVJjx495JRTTpFu3bolY1eBuOKDIwAn27CzQqYUrJay6hqpDh7UjDIgMmvJDilcXiwzJgw0HfgBALA7X8iC9soXXnhhTGOQUGvr1q1J64atJy1UUVERL38iPjjun0eck5bCB0eOU7icU/+e6gm8STNXSmlFUJr6P4/21dfZ6jp+jkywczn1OIW3cJx6g8/nk/z8fHc1wbrmmmvkRz/6kRVPDSTtg6MGv/rBMVLwq/R6vX3KvDVmewCwEy171hN4zZ121dt11JzOXgcAwO4sCYB17e8zzzwj27Zts+LpgYTjgyMAJ9O1vrrmt7ETeAfT7cJrhAEAsDNLAuDdu3eb0Uas64Ub8cERgNNpt+dYg1kNgvV+AADYmSUBcMeOHZO2lhVINj44AnA6LWnWbs+xSPHV3g8AADuzJAA+4ogjpLKyUr766isrnh5IKD44AnC6nHS/GXUUi5pQ7f0AALAzSwLgsWPHSmZmpjz++OMmEAbchA+OAJxuQG6WmfMbC+1sr/cDAEC8Pgf4YH6/X6666ip57LHH5MYbb5Szzz5bBg0aJO3bt5eUJmqucnNzk7qfQKs+OAaivw8fHAHYif4NmzAs18z5jaYRVtr+7WMNmgEA8EQAPHXq1LrvKyoq5Omnn45qHtTs2bMTvGdA6/HBEYAbTByeJ4UrSqR0X6DZOcBa+TJheO0cWQAA7MySEuiWoGkWnPbBUT8QNpcL4YMjALvqmJ0mM8YPkPZZqSbDG4ler7frdro9AAB2Z0kG+K9//asVTwsk/YPjlHlrTFOsSCWE+sFRg2Q+OAKwq96dMmXWpMFSsLRI5u6f86vdnrXhVbjsWTO/BL8AAKewJADOy6NMCu7HB0c4jQY3OsZLT9royZmWNEKC+2hwe9Wo7jJ5ZD7HBwDA8SwJgAGv4IMjnGBXebXMXVokBeEMX4qYETjh9exa0k+GD3o8DOmazQsBAHA02wTARUVFUlpaar7XbtBkieEmfHCEXW3YWSFTClZLWXVNw1L9gJguwIXLi2XGhIGmqgEAAMDJLA2Ad+3aJQsWLJD3339fvv322wNua9u2rZxwwgly3nnnSceOHS3bRwBwc+ZXg9/SimCjXX41KC4NBs16dl0LSiYYAAA4mWVdoL/88kv5xS9+Ia+88kqD4FfpdS+99JLcdNNNsmrVKkv2EQDcTMueNfPb3JRXvV3XBWsjJAAAACezJAOspc733nuvlJWVSVZWlpx55pkybNgw6dy5s7m9pKREli9fLq+99poJhHXb+++/35RGAwBaT9f66prfSB3KI6nev702QqIxFgAAcCpLAuDnn3/eBL/du3eX2267TTp16nTA7Xr9EUccIWeffbbcddddsmXLFlm4cKFMmjTJit0FANfRbs8aBMdCg2C9H42QAACAU1lSAv3pp5+ay5/+9KcNgt/69DbdRi1ZsiRp+wcAbqclzdrtORY6/1XvBwAA4FSWBMA7duyQzMxMGTx4cLPb6jYZGRmmSzQAID50zq+OOopFTaj2fgAAAE5lWROsWIVCsZXqAQAaNyA3K+a1vGl+n7kfAACAU1kSAHfp0kUqKipk9erVzW6r21RWVpr7AADiQ4PfCcNyTVAbjbT929MACwAAOJklAfCRRx5pLh999FHZs2dPk92iH3nkEfP9d77znaTtHwB4wcTheaakubkQWG/X7SYMz0vSngEAALioC/S5554rb7zxhnz99ddy/fXXmzFI2vU53BBLxyCtWLHCjEHau3evZGdny/e//30rdhUAXKtjdprMGD9ApsxbY5pbRRqJpJlfDX51O90eAADAyXwhixbXfvHFF3LfffdJeXl5k9tp8HvTTTfJ0KFDxeu2bt2atLXQeXm1mR6aj8HOOE7jY1d5tRQsLZK5y4rNaCTt9qwNr8Jlz5r5JfjlOIW78fcUTsBx6g0+n0/y8/PdFwCHM73z58+XDz/80GR662vTpo2MGjVKzj///CZHJXkJATBwIP5HGF+B/XN+NRusWd+WNMpCQxyncAKOUzgBx6k3+BIcAFtSAh3WuXNnueqqq8yXjkbSNb+qffv2NL0CgCTTYHdI12xedwAA4FqWBsD1aZdnOj0DAAAAAMTrc4ABAAAAAHBsBliXHy9atEjef/99WbduXd1IpHbt2km/fv3k+OOPl5EjR0pKCnG6F4UCAQmuWydSVi6Sky3+fv3El2qbogUAAAAADmNZNFFcXCx//vOfZe3atRFv0y8Njvv27Ss33HBD3aJ3uF/N7t1SOX++VBX+SyQQENETIDU1Iqmpkj72PMk4/3xJ6dDB6t0EAAAA4DCWBMA6+uiOO+4wQa4aOHCgHH744XXdnnfu3Cmff/65rFq1StavXy933nmn3HvvvWYkEtwtuHGjlE27XkI6Hqu6usHtVc/NkernF0rOA9PF37OnJfsIAAAAwJksCYB19JEGvzrqaNq0aXLEEUdE3G7FihUmS6yzaPU+l1xySdL3FcnN/JrgV0vhG5vOVV1tbi+bNk3aPP44mWAAAAAAUbNkce1HH31kLnX8UWPBr9KssG6jtBwa7qZlzybz29xo6lBIQmXlUrVgQbJ2DQAAAIALWBIAl5SUSGpqqmlw1RzdRrfVsmi4u+GVWfMboew5oupqqVxQaO4HAAAAALYNgLX0OT09ParuzrqNbqv3gXuZbs+xBrPhLtEAAAAAYNcAWJteaSOsLVu2NLutbqPbDho0KCn7BovoqKNYx13p9no/OE4gGJKV28tl8dffmkv9NwAAAODKJlhjx46VJUuWyD/+8Q+5+eabJS0tLeJ2gUDAbOP3+8194GI52bWjjmKh2+v94Bi7yqtl7tIiKVhWbILeuglXfp9MGJYrE4fnScfsyH8PAAAAAEdmgPv16yfXX3+9GXH0y1/+Ut58803ZsWOHCXj1S7/X6/Q23UbnAOs8YLiXv18/M+c3JqmptfeDI2zYWSGTZq6UWUt2yN7KoFQEaqS8qsZc6r/1er1dtwMAAABckwG+8MIL677X8ua//e1vTW5/3333Rbze5/PJ7NmzY3puDaiXLVsma9euNV/h5lpz5syRWE2dOtWMaGqMjnA65JBDYn5cL/Klpkr62PPMnN+oGmGlpUnGuLHmfnBG5ndKwWoprQhKY8XO1cGQlAaDMmXeGpk1aTCZYAAAAMSdo6OHUHPjciIoKCiQxYsXx3U/Tj755IjXZ2dTnhuLjPPPl+qFCyVU2sQcYOXziS8nW9LHjYv1rYJFtOy5rLqm0eA3TG8vqwpKwdIiuWpU9yTtHQAAALzCkgD4jjvuEKtoA65evXqZMuz+/fubLG51tKN3GqGPgdZL6dBBcqZPl7Jp08yc34iZ4LQ0E/zqdro97E/X+uqaX83wRqN6//aTR+abtcEAAACAowPgoUOHilVopmVv/p49pc3jj0vVggVmzq8ZjVTXKSnVlD1r5pfg1znWFO+LucuzBsF6vyFdqaIAAABA/Di6BBrupMFt5uTJknHppbVzfjUbnJNtGl7Fe81vYH+gpWW3Oel+GZCbRdYxzvS1jXnCla/2fgAAAEA8EQDHwb///W/Ztm2bGefUo0cPGTlypLRr1y4eD+1pGuymJmj+M+N4kkdPLMQ84SpUez8AAADAVQGwdmHetGmT7N27V4LBYIuaTVlt5syZB/z7ySeflMmTJ8tpp51m2T6hcTpmRzsSa1OmButSA2LG8RQuL5YZEwZK706ZvJStVJdVD0R/nzS/z9wPAAAAcEUA/NVXX8k///lP+fLLL6PaXkce2S0APuqoo+Twww83M4o147t9+3Yzv/jFF1+URx55RNq2bSvHHHNM1I+n844Plp6eLvfcc4/5Pjc3V5IldX+pcV5enrhJSVmVTP37CimtDDbaaNqM46kJyrUL1sqL142Wzjnpyd5N17nsuJ7y93c3SlWw+VRwuj9FLjuul+R36+LZ4xTuwnEKJ+A4hRNwnMKxAbAGv9oJurKysnYnUlNNAJkS60JBi/34xz8+4N9a/nzZZZdJ9+7d5dFHH5VZs2bFFAAj8Z76YKOUNRH8hunteyuC8vQHm2TaGf15a1rpslG9ZPbib2RXeU1zE66kTaZfLh3Vk9ccAAAA7giAZ8+ebYLfrl27yk9+8hPTFdppwW9TtPT5ueeeky1btsiOHTukS5fmM1nq/vvvb/L24uLiFs0+bolwRq2oqEjcQhtePfXhpqiykEq3e+rDjXLh4e1ojBUHfx3XX6bMW2OaW0UaiaRlz7ruV7erKS+VonJvHqdwH45TOAHHKZyA49QbfD6f5OfnJ+zxLYk6V61aZS6vv/56U0LspuBX6c+jwb3avXu31buDOIzjQevpeupZkwbLJSO6SJsMv2Smpkh2Woq5bJvhN9fr7ay7Bg6kf7dWbi+XxV9/ay5j/TsGAAAszgBrFjMzM1P69OkjblVWVmYuMzIyrN4V7Mc4Hut1zE6Tq0Z1l8kj8xk/BTSDbvUAALgkAO7WrZt88803UlNT47rsr/r6669N+bMGv4cccojVu4P9GMdjH9oVekjXbKt3A7AtutUDAJAYlkSfp5xyigQCAfn444/F7l5++WWZNm2aPPPMMwdcv2TJElmxYkWD7Tdu3GjW8mqWW9cCh7vVwUbjeGLAOB4AVmR+dVRbaUXk9fJ13eorgmZdvW4PAACiY0l09t3vftcEkNopuWPHjjJw4MCkPbc+77x58+r+rYG4uvXWW+uuGz9+vIwYMcJ8v2fPHpPN3bVr1wGPs3btWikoKDCL8Xv16mXGFWnDK+1wrfOMDzvsMJk0aVLSfi40T4PfCcNyzZzfxj5UHhz86vaxBs0A0BpzlxaZOeXN/ZUK7V/aUbC0yCwtAAAANg2Atez5V7/6lTz11FNy2223yZAhQ6Rfv36SlZXV5P0mTJjQ6ufWgHbNmjUNrq9/nW7TnCOPPFJKSkpk3bp1pqlXeXm52f/BgwfLCSecIKeeeqory7udbuLwPClcUSKl+wJNfrj07S+ZnjCc+bIAkkcbXBUsK47qJJ2q3r+9rqvnZB0AAM3zhZI1VydCJvbxxx+PaXyJjhbysq1btzIGKV5r66IYxzNj/AA6Etsc4xDgtuNUuzxPnbdGKgLRjWtT2kn9ofEDWFePVuHvKZyA49QbfAkeg2RJBnjlypVy3333mSZY4aZY7du3F7/fb8XuwKPjeLRscO6yYpNxSfGJ1IT+V/asmV/tWAwAyUS3egAAEsuSAFjXzmrw27dvX9NgKjwzF0gWxvEAsCO61QMA4MIAeP369ebyZz/7GcEvLMU4HgC27FZf258xKnSrBwAgepZ0adJlx9owKpG13QAAOLVbvQa10aBbPQAADgiADznkEKmqqpLqamYXAgBwcLd6LYVuLgSmWz0AAA4JgM8880wzK/edd96x4ukBALB1jwLtQt8+K7XRTLBer7frdjTsAwDA5muATznlFPniiy/kiSeekIyMDBk9erQVuwEAgC3RrR4AABcFwDNmzKh98tRU+ctf/iLPPPOM9OvXTzIzM5ucB3XNNdckcS8BALAO3eoBAHBJAPzWW28d8O/i4mLz1RwCYACA19CtHgAAhwfAJ510ksnoAgDgZoFgSJZvLpW9lQGpLi//35gjAADgnQB46tSpVjwtAABJsau8WuYuLZKCZcUSrAmJP8VnguHwmCPt9EzzKgAAPBIAAwDgVht2VsiUgtVSVl0j1cHQgTcGRGYt2SGFy4tlxoSBptkVAABw+RgkAADcmvnV4Le0Itgw+N1Pr9fbp8xbY7YHAADJQwAMAECcaNmzZn4jh77/o7eXVQWlYGkRrz0AAG4rgQ6PPWoNxiABAOxM1/jqmt/GMr8Hq96//eSR+TTGAgDATQHwwWOPWooxSAAAu1pTvM8EwbHQIFjvN6RrdsL2CwAAJDkAzs3N5TUHALialjSnxLiwKMVXez8AAOCiAPihhx5KxtMAAGCZnHS/1NTEdp+aUO39AABActAECwCAOBiQmxXzWt40v8/cDwAAJAcBMAAAcaDB74RhuSaojUba/u1jDZoBAEDLEQADABAnE4fnmZLm5kJavV23mzA8j9ceAIAkIgAGACBOOmanyYzxA6R9VmqjmWC9Xm/X7XR7AADgsiZYAAB4Re9OmTJr0mApWFokc5cVS7BGTHfoYDBUV/asmV+CXwAAko8AGACAONPg9qpR3WXyyHwpDmbI3sqAVJfvbVGjLAAAED8EwAAAJIgGu0d0a2++LyqKcUYSAACIO9YAAwAAAAA8gQAYAAAAAOAJBMAAAAAAAE+wxRrgPXv2SFFRkVRWVsrQoUOt3h0AAAAAgAtZGgAvXrxY5s6dKxs2bDD/9vl8Mnv27Lrb9+7dKw888ID5/vrrr5fs7GzL9hUAAAAA4GyWlUAXFhbKfffdVxf8qlAodMA2bdq0kfT0dFm2bJl8+OGHFuwlAAAAAMAtLAmAV69eLc8++6z4/X65/PLL5R//+Ie0b187JuJgJ554ornUIBgAAAAAAEeVQL/44ovmcuzYsTJmzJgmtw2vCf7qq6+Ssm8AAAAAAHeyJAO8atUqc3n22Wc3u227du0kIyNDdu3alYQ9AwAAAAC4VYpVXZ+zsrJMcBuNtLQ0CQQCCd8vAAAAAIB7WVICrRndffv2SU1NjaSkNB2DV1RUSFlZWdTBMpBsgWBI1hTvk7KqoOSk+2VAbpak+n28EQAAAIDNWBIAd+/eXdasWSMbN26UPn36NLntRx99ZLpD9+7dO2n7B0RjV3m1zF1aJAXLik0QrOdyamrEBL8ThuXKxOF50jE7jRcTAAAA8HIJ9NFHH20uFyxY0OR2JSUl8swzz5jvjz322KTsGxCNDTsrZNLMlTJryQ7ZWxmUikCNlFfVmEv9t16vt+t2AAAAADwcAGvzq06dOsmiRYvkr3/9q2zatKnuNl3ru3XrVlm4cKHcfPPNpvmVZoxPPvlkK3YViJj5nVKwWkorglIdPHB2dZher7dPmbfGbA8AAADAoyXQmZmZ8qtf/Uruvvtueeedd8xX2KRJkw7YtmPHjnLTTTdJaqoluwo0oGXPZdU1Ejn0/R+9XdcFFywtkqtGdeeVBAAAALyYAVa6pve+++6TU045xXR5PpgGvJr1veeee0wGGLADXeura34by/weTLcLrxEGAAAAYC1L06odOnSQa665Rq688kpZv369KXfWztB6fb9+/Uy3aMBOtNtzrMGsBsF6vyFdsxO2XwAAAABsGgDPmDHDXE6YMEG6dOliMsCDBg2yYleAmGhJczOTuxpI8dXeDwAAAIAHS6Dffvttee+99yQvL8+KpwdaTOf86qijWNSEau8HAAAAwIMBcPv27SU9PV18Pp8VTw+02IDcLDPnNxZpfp+5HwAAAAAPBsC6vre8vFx27txpxdMDLabB74RhuSaojUba/u1jDZoBAAAAuCQAHjNmjLmcM2eOFU8PtMrE4XmmpLm5kFZv1+0mDKfUHwAAAPBsAHz44YfL5ZdfLm+99Zbcf//9pgM04BQds9NkxvgB0j4rtdFMsF6vt+t2uj0AAAAAj3aBvvbaa82l3++XRYsWmS9dE9y2bVtJaaTFrq4XfvDBB5O8p0BkvTtlyqxJg6VgaZHM3T/nV7s9a8OrcNmzZn4JfgEAAACPB8BFRUUNrquqqpKSkhIrdgdoEQ1urxrVXSaPzDdzfnXUkZY8t6RRFgAAAACXBsDXXHONFU8LJIQGu0O6ZvPqAgAAADZnSQB8yimnWPG0AAAAAAAPs6QJFgAAAAAAyUYADAAAAADwBEtKoIuLi1t0v9zc3FY/t45cWrZsmaxdu9Z87dy5M24zibdu3Sq/+MUvpLq6Wo444gi57bbbWv2YAAAAAAAHB8BTp06N+T46Bmn27Nmtfu6CggJZvHixJMKjjz4qgUAgIY8NAAAAAHBgANwSoVAoLo8zcOBA6dWrl/Tr10/69+9vgnHN2LbWG2+8IZ9//rmcccYZ8vrrr8dlXwEAAAAADg+A//rXvzZ5e3l5uaxZs0ZeeOEF2bNnj1x33XVyyCGHxOW5x44dK/G2e/duefrpp2XYsGEyevRoAmAAAAAAsCFLAuC8vLxmt9Es7UknnSR33XWXPPzww3LvvfeKXf3zn/+UqqoqufLKK6WkpMTq3QEAAAAAOK0LdHp6ukyePNlkWOfPny92tGTJEnn//fdl3Lhx0q1bN6t3BwAAAADgxABY9e3bVzIyMuSTTz4Ru6moqJB//OMf0r1794SUVgMAAAAAPNQEq6amxnzt2rVL7Ea7UhcVFckdd9whqamtfylvuOGGiFnwe+65J25joKIV/nmiKVcHrMJxCifgOIUTcJzCCThO4YkMsHZW1i7NOTk5Yifr1q2Tl156yaxTPuyww6zeHQAAAACAUzPAOk/3o48+kqeeesr8+/DDDxe7CAaD8sgjj5ig/LLLLovb495///1N3l5cXBy3cVDNCWd+NcMN2BXHKZyA4xROwHEKJ+A49Qafzyf5+fnuCoCvvfbaJm/XjG9paWldsJednS0TJkwQu9BOzxs2bJAOHTo0CFrLysrM5fr16+U3v/mN+T58CbhBIBiSNcX7pKwqKDnpfhmQmyWpfp/VuwUAAADYMwCOJas4ePBg0wlaG03ZjXan1q9INBD+4osvkr5PQKLsKq+WuUuLpGBZsQmCU1J0jb6Y4HfCsFyZODxPOman8QYAAADAtiwJgK+55pomb/f7/aa8uHfv3tKpUyexmy5dusicOXMaXbN85513yhFHHCG33XZb0vcNSIQNOytkSsFqKauukergQWX4AZFZS3ZI4fJimTFhoPTulMmbAAAAAFuyJAA+5ZRTxClefvll8zVy5Ei5+OKLrd4dwJLMrwa/pRVBaWwFugbFpcGgTJm3RmZNGkwmGAAAALZk2yZYibJkyRKZN2/eAc221K233lp33fjx42XEiBHm+z179siWLVtsOYYJSAYte9bMb3Pt1/R2XRdcsLRIrhplvyULAAAAgGVNsNq3by933313VNvffvvtJgB98MEHW/3cGtCuWbOmwfX1r9NtANQ2vNI1vw3KnhtRvX/7ySPzaYwFAAAA27GsCZZ2eo6l67KOAIpX+XUsJdgXXHCB+YqWzgRubH0w4DTa7VmD4FhoEKz3G9I1O2H7BQAAALREijhATU2NpGjLWQBJpSXNsf7qpfhq7wcAAADYje2jyqqqKjMTODOTzrJAsumcXx11FIuaUO39AAAAAE+WQGv58o4dOw64TptPrVy5UkKhxssrdZbuO++8I8FgUHr27JmEPQUaCuwv6dWspgZ2A3KzPLO+te5nre0VF5U0v8/cDwAAAPBkAPzmm29KQUHBAdft3btXfvOb30T9GGeeeWYC9gxoevyPdkDWpk4aBGspsGZDNSCcMCxXJg7Pc/24n/DPqnN+o2mElbZ/e6+cIAAAAICz2HoMUnZ2tvTo0cMEvyeccILVuwMP2bCzwsy+1fE/DQK/gJiAsHB5scyYMFB6d3J3eb4G+oUrSqR0X6DJUUga8mqGfMLwvCTuHQAAABA9X6ipGuQEufDCC6VDhw7yyCOPJPupHW3r1q1NlozHU15eXl3Hbi9mfifNXCmlFcFmA772Wakya9Jg12eCzQmBeWtMGXikTLBmfjX4nTF+QFJPCHj5OIVzcJzCCThO4QQcp97g8/kkPz/fXU2wTjrpJBk1apQVTw00S8ueNfPb3KkGvV0DwoKl7g++NKjVQP+SEV2kTYZfMlNTJDstxVy2zfCb6/V2t2fDAQAA4GyWlEBPnTrViqcFmqVrfXXNbzTrXVX1/u0nj8x3/bpXzXJfNaq7+Vm92hQMAAAAzmbrNcBAsmlgp0FwLDQI1vsN6ZotXqDBrld+VgAAALiL5QFwSUmJrFq1Snbu3CkVFRVNbjthwoSk7RfsLxQISHDdOpGycpGcbPH36ye+1NYd0prV1G7PsUjx1d4PAAAAgL1ZFgDv2bNHHnvsMfn444+jbuxEAAxVs3u3VM6fL1WF/9KB0vK/+USpkj72PMk4/3xJ6dChRS+WlvTqQ8WiJlR7PwAAAAD2ZkkArJneO++8U7755htJTU2V3r17y9q1a833/fv3l927d8u2bdvMtm3atJGePXtasZuwoeDGjVI27XoJlZeLVFc3uL3quTlS/fxCyXlguvhbcNzUrWcNRH8f7YCs9wMAAABgb5YEwK+88ooJfrt37y633367dOzY0YxG0mBXA+PwWJNZs2bJhx9+KMOHD5exY8dasauwWebXBL979og0VjVQXW1uL5s2Tdo8/njMmWANficMyzVzfqNphJW2f3uaQAEAAAD2Z8kYpI8++shcXnzxxSb4bWzO17Rp08y4pGeffVaWL1+e5L2E3WjZs8n8NlcyHwpJqKxcqhYsaNHzTByeZ0qam+trrLfrdhOG186iBQAAAGBvlgTAW7ZsMZdHHnnkAdcHdD3nQX74wx+ay5deeilJewe7Nrwya34jlD1HVF0tlQsKzf1aMu5nxvgB0j4r1WR4I9Hr9XbdTrcHAAAAYH+WBMBVVVWm3Dkt7X+Bg34fqQt0ly5dJDs7W9Zpt194lun2HGswG+4S3QK9O2XKrEmD5ZIRXaRNhl8yU1MkOy3FXLbN8Jvr9XbdDgAAAIAzWLIGuEOHDvLtt98ecF27du3MSCT96ty5c931NTU1JjCurKy0YE9hGzrqKOb5RCm192shzexeNaq7TB6Zb+b86qgjLXmua5QFAAAAwFEsyQDn5uaagLa0tLTuOu0EXX99cNjixYtNENy+ffuk7ydsJCe7dtRRLHR7vV8rabA7pGu2HN2jrbkk+AUAAACcyZIM8IABA+TLL7+UlStXynHHHWeuO/744+WTTz6RZ555Rqqrq01AvGHDBpk/f37E9cLwFn+/fmbOb0xSU2vvBwAAAABWZYCPPfZYc/n222/XXTd69GgZOnSoWR+s44/uvvtuc7lv3z5TMj1x4kTeMA/zpaZK+tjzdLF4dHdIS5OMcWPN/QAAAADA0gzwc889d8B1Pp9PbrnlFpk3b568//77Zi2wNr/SzO9FF10knTp14h3zuIzzz5fqhQslVNrEHGDl84kvJ1vSx41L5u4BAAAAsDlbpcfS09PN2KPw6COgvpQOHSRn+nQpmzbNzPmNOBIpLc0Ev7qdbg8AAAAAtgyAgeb4e/aUNo8/LlULFpg5v2Y0knZ71oZXqamm7FkzvwS/AAAAAGwZAIdCITMWSdf/aodooCka3GZOniwZl15aO+dXs8E52abhFWt+AQAAANgyAF6/fr3p8rx8+XIz61fXAc+ePbvu9r1795qu0OpHP/qRKZEGwjTYTR00iBcEAAAAgL0DYO0A/be//U2CweABmeD62rRpI9u3b5cVK1bIYYcdZjpFAwAAAADgmDFI33zzjTzyyCMm+D3nnHPk97//vbRt2zbitieffLK5/PTTT5O8lwAAAAAAN7EkA7xw4UIJBALy3e9+15Q2qxRtZBTB4Ycfbi6/+uqrpO4jAAAAAMBdLMkAf/755+byvPPOa3Zbnf+ra3+Li4uTsGcAAAAAALeyJADeuXOnZGZmSufOnaPaXgNg7RANAAAAAICjAuC0tDRTAn1w06tIqqurpby8XHJycpKybwAAAAAAd7IkAO7atasJgLdu3drstkuXLpWamhrp0aNHUvYNAAAAAOBOlgTA3/nOd8zlCy+80OR2+/btk1mzZpnvjzrqqKTsGwAAAADAnSwJgMeMGSPZ2dny+uuvy+zZs6WsrOyA23W976JFi+TXv/61bNmyRTp06CBnnHGGFbsKAAAAAHAJXyiahbgJsGzZMrnvvvtMsOv3+816YC111mB3z5495nulzbJuvfVWGThwoHidlown6+3Ky8szl0VFRUl5PqAlOE7hBByncAKOUzgBx6k3+Hw+yc/Pd1cGWA0bNkzuvvtuOeywwyQYDNYFvLt37677fujQofK73/2O4BfwmEAwJCu3l8vir781l/pvAAAAoLVSxUI9e/aU22+/3WQZV61aZcYjhbPAgwcPlm7dulm5ewCSbFd5tcxdWiQFy4pN0JuSIqLnw1L9PpkwLFcmDs+TjtlpvC8AAABwXgBcv5whXNIAwJs27KyQKQWrpay6RqoPzvgGRGYt2SGFy4tlxoSB0rtTplW7CQAAAAdLSgn0hRdeKD/96U8j3vbNN9/Ixo0bk7EbAGyc+dXgt7Qi2DD43U+v19unzFtjtgcAAAAcswY47Le//a386le/sno3AFhIy54189vcSl+9vawqKAVLac4GAAAABwbAyqJG1ABsQNf66prfxjK/B9PtwmuEAQAAAMcFwAC8a03xvpiDWQ2C9X4AAABALAiAAVhKS5q123MsUny19wMAAABiQQAMwFI56X4z6igWNaHa+wEAAACxIAAGYKkBuVlmzm8s0vw+cz8AAAAgFgTAACylwe+EYbkmqI1G2v7tYw2aAQAAgNRkvQS7d+8284Ab09RtyufzyezZsxOwZwCsNnF4nhSuKJHSfYEmRyFpyKulzxOG5yVx7wAAAOAWjskAMyoJcK+O2WkyY/wAaZ+V2mgmWK/X23U73R4AAACwZQZ4woQJyXgaAA7Wu1OmzJo0WAqWFsnc/XN+tduzNrwKlz1r5pfgFwAAALYOgCdOnJiMpwHgcBrcXjWqu0wemW/m/OqoIy15bkmjLAAAAMCyNcAAvEEzt60NXnX7IV2zE7aPAAAA8CYCYABxsau8WuYuLZKCcPlyipj5vuEuz9roivJlAAAAWIkAGECrbdhZIVMKVktZdY1UBw/q4xwQmbVkhxQuL5YZEwaatb4AAACAFRzTBRqAfTO/GvyWVgQbBr/76fV6+5R5a8z2AAAAgBU8lwFev369LFu2TNauXWu+du7caa6fM2dOTI8TDAZl3rx5sm7dOtm8ebPs2bPHXNe5c2cZNmyYnHfeeZKXx6xSuJ+WPWvmt6n5vUpv13XB2uVZG10BAAAAyea5ALigoEAWL17c6seprq42j5WZmSm9evWSvn37SiAQkA0bNsirr74q77zzjtx+++3Sr1+/uOw3YEe61lfX/DaW+T1Y9f7ttcszXZ0BAACQbJ4LgAcOHGgCVg1M+/fvL1OnTjXBbKzS0tLkt7/9rQwYMED8fn/d9TU1NTJ79mwpLCyUxx57TO655544/wSAfWi3Zw2CY6FBsN6PLs8AAABINs8FwGPHjo3L42jQO3jw4AbXp6SkyIUXXigvvPCCKbcuLy+X7GzGucCdtKRZuz3HIsVXez8AAAAg2TwXACeDz+czgbBepqbyEsO9dM6vjjqKRU2o9n5w5wxnAAAAOyM6i7NQKGTKnysrK+Xwww+X9PT0eD8FYBt1AVIg+vuk+X3mfrAPZjgDAACvIACOg5kzZ0ppaans27dPNm7cKNu3b5dDDjlErr766ng8PGBbGvxOGJZr5vxG0wgrbf/2ZBXtgxnOAADASwiA42DRokUm6A3TJlvXXXeddOnSJabHueGGGxpcpxnkcCOt3NxcSZZw6TajnNCcq09vL//+YqfJIoaaiIF9PpG2many09OHSOec+FRGcJy2TklZlUz9+woprQw2+t6ZGc41Qbl2wVp58brRcXvvvITjFE7AcQon4DhFPMTYvgaRPPjgg2aO8N///nf59a9/bRpk/epXv5L//ve/vGBwPQ2InrlipHTMTpN0f+Q/KXq93q7b2SmAqg7WyPLNpfLB+hJzqf/2kqc+2ChlTQS/YXr73oqgPP3BpmTtGgAAQEL4Qrpo1cMmTZpkxiBpABsv2vn5xhtvlG+//VamT58et8zt1q1bzRrjZAhnfouKipLyfHA+zQAXLC2SucuKTTMl7fasDa/CZc8ThueZINgOxylrXmsbXo35+3LZWxl9R+62GX554cojKGFP0nEKJBPHKZyA49QbfD6f5OfnJ+zxKYFOAB17dNRRR8mrr74qy5Ytk9NOOy0RTwPYiga3V43qLpNH5tu6kzBrXmsxwxkAAHgRJdAJ0q5dO3O5Z8+eRD0FYEsa7A7pmi1H92hrLu0U/Grmd0rBaimtCDbatMusea0IypR5a8z2bsUMZwAA4EUEwAnyxRdfmMtu3bol6ikAxGju0iIpq66R5hYShPYHiFrS7VbMcAYAAF5EANyMl19+WaZNmybPPPPMAdcvWbJEVq1a1WB7nf/77LPPmgC4Q4cOcuSRR8b3HQPQ4jWvBcuKoxrXpKr3b6/3c6OWlKYzwxkAADid59YAa+A6b968un8HAgFzeeutt9ZdN378eBkxYkRdCfOWLVtk165dBzzO2rVrpaCgQDp16iS9e/c26353794tGzZskL1795p/X3/99ZKZmZm0nw1A41jzeiBmOAMAAC/yXACsAe2aNWsaXF//umjW7R577LFSUVEhK1eulHXr1pmgV2f2asnzGWecIeecc4507Ngx7vsPoGVY89rQxOF5UriiREr3BZosC/ftL5nWTt4AAABO5vkxSE7CGCSg5eMQVm4vl6nz1khFIPpZv5mpKfLQ+AGmmZdbma7Y89aYEwSRysO17FmD3xnjB0jvTlS0tARjO+AEHKdwAo5Tb/AxBgkA4rjmtXbVQ1S8sOZVg9pZkwYnfYYzAACAFTxXAg3Am1jz6vwZzgAAAK1FAAzAM1jzGt0MZwAAALdiDBIAz9BMp65lbZ+Vasp7I9Hr9XbdjrJfAAAAdyEDDMBTWPMKAPCCUCjk2p/JjT+b25ta2QkBMADPYc0rAMBtNCjct2+fVFVVSSAQQ8dHB9m1a5e5rKmJfqID7BEAp6enS1ZWlqSmWh9+Wr8HAGAR1rwCANxAA8LS0lJJSUkxQUZaWprtsm7xEA6e3Brgu/n4rKioMMdo+/btLQ+CCYABAAAAB9PMrwYVbdq0cWXgGxb+2dz8M7qR3++XnJycumO1bdu2lu4PATDgEqFAQILr1omUlYvkZIu/Xz/x2aDMBAAAJJZm1zSzRmAIO8vIyDBZYKvx6RhwuJrdu6Vy/nypKvyX1gSJpKRorYnWCUn62PMk4/zzJaVDB6t3EwAAJGjtr35plg2wM7/fX3e8WnmyhgAYcLDgxo1SNu16CZWXi1RXN7i96rk5Uv38Qsl5YLr4e/a0ZB8BAAAAu2AOMODgzK8JfvfsiRj8GtXV5vayadPM9gAAAICXEQADDqVlzybz29wsPC01KSuXqgULkrVrAAAAgC1RAg04tOGVWfPbWOb3YNXVUrmgUDIuvZTGWAAAwLUOOeSQmLY/9NBDZdGiRY3ePm3aNJk7d675Ov7441u9b809HxKPABhwINPtOdYZePu7RKcOGpSo3QIAALDUxIkTG1z38ccfy4YNG2To0KFy2GGHHXBbp06dkrh3sAMCYMCJdNSRdnuOhW6v9wMAAHCp6dOnR8ziagB89tlny4033hjT491yyy1y7bXXxpxZhn0RAANOlJNdO+ooFrq93g8AAABR6dq1q/mCe9AEC3Agf79+Zs5vTFJTa+8HAADQCoFgSFZuL5fFX39rLvXfTvTcc8+ZzO6f/vQnWbdunVxzzTUyfPhws0735Zdfrsse6zbvv//+AfddsWKF/O53vzNZ5SOOOEL69Okjo0aNMhnjbdu2WfQTIRpkgAEH8qWmSvrY88yc36gaYaWlSca4sTTAAgAALbarvFrmLi2SgmXFJujV1VVaYJbq98mEYbkycXiedMxOc9wrrMHv9773PenYsaNpdFVaWiqpzSQaHnroIXnxxRdlyJAhMnLkSHPd559/Lk899ZS88sor5rZu3bol6SdALAiAAYfKOP98qV64UEKle5oeheTziS8nW9LHjUvm7gEAABfZsLNCphSslrLqGqk+OOMbEJm1ZIcULi+WGRMGSu9OmeIk//rXv2Ty5Mly5513it/vj+o+l1xyifz2t7+VvLy8uutqamrkgQcekD/+8Y9y7733yv3335/AvUZLUQINOFRKhw6SM326+Nq3MxneiNLSzO26nW7vpDFPgVWrJLDkU3Op/wYAANZlfjX4La0INgx+99Pr9fYp89aY7Z2kc+fOcuutt0Yd/KrRo0cfEPyqlJQUuf76603m99VXX03AniIeyAADDubv2VPaPP64VC1YYOb8mtFIdfVIqabsWTO/Tgl+a3bvlsr582tnHB/0s2jJt2a9nfKzAADgFlr2rJnf5lb66u1lVUEpWFokV43qLk5x4oknSlZWVsz327lzp7z22mvy5Zdfyp49eyQYDJrrA4GA7Nq1y3xpWTXshQAYcDgNCDMnT5aMSy+tnQ+so45ysk3DK10r7BTBjRulbNr1Eiovj7iuWdc7Vz+/UHIemG4CfwAAkHi61lfX/DaW+T1Y9f7tJ4/MN2uDnaAlI44KCwvll7/8pZSVlTW6jd5GAGw/lEADLqHBbuqgQZI64jvm0knBr2Z+TfC7Z0/jTb2qq83tZdOmme0BAEDirSneF3OXZw2C9X5OkZGREdP233zzjSl1rq6uNuuG3333XVm7dq1s3rzZfB111FFmu1BTPVpgGQJgAJbTsmeT+W3ufxShkITKyk3JNwAASDwtadYVSbFI8dXez63+85//SFVVlfz4xz+WK6+80oxAql9CvWnTJkv3D00jAAZgKW1wZdb8RjPOSVVXm/XONMYCACDxctL9ph1HLGpCtfdzKx2TpPLz8xvc9uGHH0pRUZEFe4VoEQADsJRZtxxrl+dAoPZ+AAAgoQbkZsW8ljfN7zP3c6u+ffuay/nz50u5VrDtt3XrVrn55pst3DNEwzmLBAG4kzbtirm2KqX2fgAAIKE0+J0wLNfM+Y2mEVba/u2d0gCrJc466ywZNGiQLF261IxDOvroo6WyslLef/99Oeyww8y/Fy9ebPVuohFkgAFYKye7dtRRLHR7vR8AAEi4icPzTElzcyGt3q7bTRh+4Hxct0lPTzfZ38suu8w00NI1wdoES9cEz549W9LS0qzeRTTBF6I9mWNoWUWy3q7wYG/WMCDRdC3vnvPHi+zdG/2d2rSRdvPnSZf9a284TmFn/D2FE3CcOpd+NiwpKZHOnTuLz5e4rOuGnRUyZd4a09wqUiZYM78a/M4YP0B6d8pMyD6k7p9woXN24d5j1efzRVxfHS9kgAFYSsc1pY89TyTas6VpaZIxbqyjxjwBAOB0GtTOmjRYLhnRRdpk+CUzNUWy01LMZdsMv7leb09U8AvEC58gAVgu4/zzpXrhQgmV7ml6FJLPJ76cbEkfNy6ZuwcAAESkY3aaXDWqu0wemW/m/Go2WLO+LWmUBViFDDAAy6V06CA506eLr327xjPBaWnmdt1OtwcAANbQYHdI12w5ukdbc0nwCychAwzAFvw9e0qbxx+XqgULzJxfMxpJuz1rw6vUVFP2rJlfgl8AAAC0FAEwANvQ4DZz8mTJuPTS2jm/OuooJ1v8/fqx5hcAAACtRgAMwHa0wVXqoEFW7wYAAABchjXAAAAAAABPIAAGAAAAAHgCATAAAAAAwBMIgAEAAAAAnkAADAAAAADwBAJgAAAAAIAnEAADAAAAADyBABgAAAAA4AmpVu8AAAAAAMTTIYcc0uC61NRU6dy5sxx11FHyk5/8RI455hjbv+jvv/++TJw4Mab76PbTp09v9PZjjz1WvvnmG9m8eXNc9q2557MbAmAAAAAArlQ/eCwrK5MvvvhCXnzxRXnppZfkwQcflHHjxiVtX77++ms57rjjZNSoUVJQUBDVfbp06RIxAH7hhRekvLxcTjnlFMnLyzvgtpEjR8Ztn92IABgAAACAKx2cmaypqZF77rlHHnroIbntttvk+9//vqSlpYld9e/fP2J29YMPPjAB8NSpU+X444+P6TGfe+45CQQC4lWsAQYAAAAQtVAgIIFVqySw5FNzqf92ipSUFPnFL35hyqF37dolq1atEq/p3bu3Cay9igwwAAAAgGbV7N4tlfPnS1Xhv0Q06E1J0ZSqLq6V9LHnScb550tKhw62fyXT09Olbdu2JgAOBoMNbt+3b5/8/e9/l+eff16++uorc92gQYPksssukwsuuKDB9rqe9q9//au8++67snXrVsnMzDSly7rGWNcaa7D5pz/9Se6///667G39NcrxXEM7bdo0mTt3rvmqqqoy+/X555/Lnj17TPl3+/btG10D/Prrr5vy8E8++US2bdtmXhsNln/wgx/IT3/6U8nIyBA3IAAGAAAA0KTgxo1SNu16CZWXi1RXN7i96rk5Uv38Qsl5YLr4e/a09au5adMmE/xq6bMGePUVFxfLRRddJCtXrjRBrK7ZVYsXL5brr79eli1bJr/73e/qttcg8rvf/a7s3r1b+vTpI6eddpops9YA85lnnjENtzQAPuyww2TMmDEmwNQ1u7p2N5FrdgsLC83zDx8+XE499VTZuHGj+Hy+Ju+jmfGKigoT7A8ZMkS+/fZb+eyzz+QPf/iDCe6fffZZ8fv94nQEwAAAQ0vYguvWiZSVi+Rki79fP/Gl8r8JAPA6zfya4HfPHpFQKPJG1dXm9rJp06TN44/bMhOsTbA0G/qb3/zG/FszupoRrU+DXA1+r7jiCrn11lvrsp5FRUVy+eWXyxNPPCGnn366CSqVBoUa/E6ePPmAwDgcHFfvP1lw9tlnmyBYA+DG1vXG06xZs2TGjBly3nnnRX2fP/zhD3LSSSdJVlZW3XV79+4164w1Ozx//vyYO1LbEZ9sAMDj3FLSBgBIDP1/hMn8Nhb8hoVCEiorl6oFCyRz8mTbjkNq06aNCVZ/9KMfHXD9ihUr5I033pAjjzzSBMm6XjhMs7b33nuvyfY+9dRTdQFwSUmJuTzxxBOjeu5k0SA9luBX6c8W6bXS10ID4FdffZUAGADgbG4qaQMAJKY6yJwgjfD/iIiqq6VyQaFkXHqpLaqI6mcsdU2sliZ/+umn8uc//1l69eplSpbD3n777bpAsH7wG3b44YdLTk6OKQsOGzZsmLnUztJ6Hw2EdQ2w1c4666wW3W/9+vXmJMCGDRtMl2kt5w7tP/ERXg/tdNYflQAAS7ilpA0AkDhmaUysXZ73L6lJHTRIrBap1FgzvePHjzdly//5z3/qOiLrnN5wKbB+NaaysrLue22K9dZbb5mGWZpR1uBX193qGl9dS6zriK0Qa/Y5FArJb3/7W3nsscfqAt6DaTm0GxAAA4BHObmkDQCQJNoXIkI2tEm6vd7PpjSTe8kll8jf/vY3U86sgZ/SbGe4KZVmh6OhTaH0ca699lp55ZVX5L333jMZ5kWLFplZwzNnzjTdoJMt1o7N//73v+XRRx+V7t27m5Jnbd7VuXNn0yhMM+fa4KuxwNhpPBcAa1pfu7etXbvWfO3cudNcP2fOnJgX0evBrR3h1qxZYx5HD5BDDz1UTjjhBFN2oPPFAMCOnF7SBgBIkpzs2r4QsdDt9X421qNHjwZlvfn5+XUl0FdffXXMQbV+3XjjjaZ7so490myqBpMvvPCC2N1LL71kLn//+9/LGWec0aBrtpvEeDrH+QoKCkxL8I8++qgu+G0JLXP4y1/+YuZ46eJwnael5RPaYvyf//ynOZNUvzwCANxS0gYA8A6dCKBNEWOSmlp7PxsLB3W6pjdMOyCrl19+uVWPrTOGb7nlFjN26Msvv6y7XpNlKhDr/3+ToLS09ICTAAfHPW7iuQB44MCBpub/l7/8pUnzhw/ElpQV6FBoLW3QNQI6dPr222+XP/7xj5Kbm2sO9nnz5sV9/wEgLlxY0gYAiD/f/okAEu1n5rQ0yRg31tbVQroGWMcEqfpNsEaMGGGC4I8//lh+/etfm0zuwXSM0ptvvnlAcq1+kBum22jJsJYUh3Xq1MnEHpowCwaDYid9+/Y1l/q61C911lLuhx9+WNzEvkdmgowdOzYujzNu3LiI1+tZk4svvthkh3UNgH4PALbj0pI2AED86Ti86oULJVTaRNNE5fOJLydb0hv5nGwFTVKF6Uxe7QK9ZMkSs973zDPPlAkTJhyw/YMPPiiTJk2SJ598UgoLC2Xo0KHSrVs32bNnj5kPvGXLFjMjODwGSef6/vznP5fevXvL4MGDTRMszS7rUkntCq1Jt7D09HTTHOu1114zz60l03qdrhG+8MILxUo//vGPzZJQ/bm1wnXIkCGybds2UzX705/+1KxzdgvPBcDJoL8AateuXVbvCgB4qqQNABB/OgEgZ/p0MxFAmyJG7B+RlmaCX93OThMD5s6dW/e9BqTt2rUzSxc18NUOzgePO9JKzn/9619myaReasb3k08+MddrYywNfrUKNOwnP/mJSYBp1lizpfv27ZOuXbuabTRw1I7Q9Wm1qC6VfOedd0yArZlgLYm2OgDu16+fWQes85E1eNeZv3qdVrrqCQE3BcC+kFvaebWQvqF6NijWJlhN0V8SPVj0F2XGjBlxe9ytW7cmrfuaDvtWRUVFSXk+oCU4Tltn3+OPmzm/UTXC0pK2iy6kC3QLcJzCCThOnUs/G5aUlJiOvbrmNNHj83QigDZFNH0kNHjU6qDUVFP2rJnfRAa/4QazdlxDi/gdq3pbpLXI8UIGOAG0FEIdffTRiXh4ABCvl7QBAJJPg1sdh6cTAUxTRM0G52Sb6iA7r/kF6uNIjTMtF1i+fLnpKBfreuMbbrihwXW6LuCee+4x32tGOVnCZ9jCZ4QBO+I4baW8POn45FOy+Uc/kpqyMglVVTXYxJeeLik5OXLIP/8p6X37tPYZPYnjFE7AcersrJouu9P3MNEZ4DqpqZJ22GGSbOGfj1Gjzj1WU1JSTHyRtGM1As91gU4kXRivI5D0Db3mmmtMpzcAsDMNansWLpAOkydLStu24svKEl9OjrlMadfOXK+3E/wCAAA3IAMcJ9rt7d577zVrEiZPniwjR46M+THuv//+Jm8vLi5mDTBQD2vW4ujCC6TN+PMblLTVpKbKTh3VQD+AFuM4hRNwnDo7q6YdjfUzqJVZtWRgDbA7jtWioiLWADvdjh075O6775aysjKZOHGinHPOOVbvEgDETNdvpQ4axCsHAABcixLoVtI1F3fddZe5HDNmjAmAAQAAAAD2QwDcCnv37jWZ3+3bt5uh1pdffnn83hkAAAAAQFwRADfj5ZdflmnTpplh2PVVVlaa7sy69nfUqFFy9dVXu37dBQAAAAA4meeaYC1ZskTmzZtX9+/wIO1bb7217rrx48fLiBEjzPd79uyRLVu2mBLn+p599llZvXq1aeXt9/vl4Ycfjvh8U6dOTdBPAgAAAK/TBIx+BYNBxgPB1oLBYN3xaiXPBcAa0K5Zs6bB9fWv022aow2vlHYye/fddxvdjgAYAAAAiZSZmSn79u2TNm3aWB5cAI3RCtr09HSxmi+k/ajhCFu3bmUMElAPYzvgBByncAKOU2fThExpaampTNRgOC0tzZWBMGOQnJv5rayslIqKCmnfvn2zlQp67Obn5ydsfzyXAQYAAADcRAPfDh06mCywfn377bfi1p8zHPDDOXw+n8n8RhP8JoP1ewAAAACg1UFGdna2+VJuLPKkUsGZfDarRiAABgAAAFzGbkFHPH8mN/5sSB7GIAEAAAAAPIEAGAAAAADgCQTAAAAAAABPIAAGAAAAAHgCATAAAAAAwBMIgAEAAAAAnsAYJAexouU7bebhBByncAKOUzgBxymcgOPU3XwJjnl8ITdOyQYAAAAA4CCUQAMAAAAAPIEAGBHdfPPN5guwM45TOAHHKZyA4xROwHGKeGANMCKqqqrilYHtcZzCCThO4QQcp3ACjlPEAxlgAAAAAIAnEAADAAAAADyBABgAAAAA4AkEwAAAAAAAT2AOMAAAAADAE8gAAwAAAAA8gQAYAAAAAOAJBMAAAAAAAE8gAAYAAAAAeAIBMAAAAADAEwiAAQAAAACekGr1DiA5qqqqZMGCBfL+++9LcXGxtGnTRoYPHy4XXXSRdOrUKabH2rt3r8ydO1c+/vhj2b17t3To0EFGjhwpEydOlJycnIT9DHC/eBynZWVl8umnn8rixYtlzZo1snPnTklLS5NDDz1UTjjhBDnrrLMkNZU/fbD2OI1k69at8otf/EKqq6vliCOOkNtuu423CbY5Tnfs2CGFhYWydOlS2bVrl2RlZUm3bt3k2GOPlR/84Ae8U7D8OF22bJm88MILsnbtWikvLzfHaN++fc3/9/VzKhDGHGCP/HG58847TTDQsWNHGTx4sBQVFZk/EO3atZO7775bunbtGtVj7dmzR/7f//t/sm3bNnMf/cPyzTffyNdffy35+fnmsfSPF2DVcTp79myZP3+++Hw+6d27tzku9bhdtWqVCSz0cW+99VbJyMjgTYJlx2kk+rhffPGFhEIhAmDY6jjVk4r333+/edw+ffqYwFdPhm/atMn8LX3wwQd5x2DpcaqB75NPPmn+3z9w4EDp3LmzlJSUyOrVq83f1HHjxskPf/hD3iUYpEE8QIMB/eOifxA0eM3MzDTXL1y4UJ566il5+OGH5Te/+U1Uj/XPf/7TBL96Ju36668Xv99vrn/88cfl5ZdfNn98pk6dmtCfB+4Ur+NUP4xpNuLss8+W3NzcA7Jrd911l3z55Zcyb948ufjiixP688Cd4vn3tL433nhDPv/8cznjjDPk9ddfT8Cew0vieZxu3rxZ/vjHP5psmj7WoEGD6m6rqamRr776KmE/B9wtXsepnuR+5plnzGdSrZwZOnRo3W16UlEDaa1eOO2001p8ghLuwhpglwsEAiYwVVdccUXdHxf1/e9/X3r16mX+OKxfv77Zx9KSp/fee8+Uj1555ZV1wa+69NJLzdm6d955R0pLSxP008Ct4nmc6lneSy655IDgV2kmOBz06nEMWHmc1qdLSZ5++mkZNmyYjB49mjcGtjpONRDR6pkpU6YcEPyqlJQU6devH+8YLD1ONYjWY/Twww8/IPhV+m8tqdYs8Lp163inYBAAu5xmu3QdhJ7x0rKlg+naHaXrJZvz2WefmT8gQ4YMMet+69M1lkcddZQ5G6ylUoBVx2lTtCQ6fDIHsMtxqpU1WgqoJxYBOx2nuiZT/9+vjzVixAjeHNjyONXPoNFo27ZtC/YUbkQJtMtt3LjRXEb646J0Da/SdTytfSy9/s0336zbDrDiOG3K9u3bzeXBJ3AAq47TJUuWmOYvF1xwgVlXqWvWALscp+E16VqiGgwGZdGiRaafgp7s7tGjhxx//PH0/YDlx2n//v1NE9YVK1aYY/bgEmht3KZVYJrAARQBsMvp2VulzQAiCXfY06YD0T5WY135ws8RzWMBiTpOm/Liiy+ay6OPPpo3AJYfpxUVFfKPf/xDunfvLmPHjuUdge2OU21yqbQ89fbbbzelpgc3HbzhhhtM6Slg1XGanZ0tV199tfzlL38xTbUOboKlpfvXXnstEyBQhxJol9MPWKqxjrfhNRfh7VrzWOHro3ksIFHHaWNeffVVWb58uTlLTLABOxynGjzoh7urrrqKD2aw5XGqnZ7DTdq2bNkiP/vZz0zTy+nTp8uJJ55obtcGWTpuDrDqOA2XTN9yyy2mIkGrFLSyRi/1cbS/QmtG1MF9yAADcL2VK1eadZY6HuGaa67hf4SwnDZjeemll+Skk06Sww47zOrdASLS8mel5c96okZLnpUGGdddd50JivVYfuWVVxgxA0s9//zzMnPmTDnmmGPMkpIuXbqY2dXPPfeczJkzx4xWuvnmm3mXYJABdrnwGbTKysqIt4fPrNXvvtfSxwpfH81jAYk6Tg+m64fuvfde03HyRz/6kRnhBVh5nGow8cgjj5hqhMsuu4w3A7b//75ejho1qsHtp556at06S8Cq41RHyGknfW10qSX5PXv2NPfTyxtvvNFcr/0WaNKKMDLALhceBdNYY5Vw2VJeXl7Uj9VYqVP4OaJ5LCBRx2l9evZX5/+VlZXJxIkT5ZxzzuGFh+XHqd5/w4YNphnb/ffff8BteqwqHf0Rnn/ZkrnC8K54/j0Nb6OPqRU0jd2uc1gBq47Tt99+21zqCW4dzVWf/luv17+5Wg32ne98hzcKBMBup3PUVGOD6sPz1fQsWWsfK3x9eDvAiuM0TEcd3XXXXeZyzJgxJgAG7HSc6vxf/YpEA2GyarD6OA2PjgufmGlsjTCVX7DyOA0Hy9oMK5Lw9eHjFSAD7HKDBw82v/g6/kXPfoX/ZxamIw2i7Yp75JFHmjPAegattLRU2rdvX3ebDiD/5JNPzJk2zq7ByuM0/D85zfzq451yyily+eWX86bANseprk3TNWmNlfJpF9MjjjhCbrvtNt41WHacKu2eq7NT9USNrvfVjuX1hU/SHPwcQDKP0/DnUV2PHkn4ev3bCyjWALtcamqqnH322eZ7HbdRv5vewoULzRw2nZcWnremXn75ZZk2bZo888wzBzxWx44dZfTo0WYt5d///nezji1MGw9oCZR2hawfGAPJPk51PdE999xj1v7qmjUdjRCpdA+w8jgFnHCc+v1++d73vmeaYeljlZeX1922bNky+e9//2v+vp555pm8obDsOA339nj33XdNMqa+jz/+2Fyvxyk9QBBGBtgDzj//fDP+RdvB//znPzdn3XT+ms7za9eunemKW58GsnqmV0tHD6ZNhPR+emZO/wj169dPvv76a/OlQ8bJtMHq4/TZZ581c/+0GkE/vD388MMRn2/q1Km8WbDsOAWccpz+//bOA0iKqmvD14T5MwfMAcyKigHMES1FTCiYE4KoKEGCiiBaSlJBRBRUFFGMgAkTKAIGxIhiTpjFrJgD/vXcqrP/pemenZ6ww26/T9UUYWd6u2+a855z7rktWrTwmQlcj2s1bNjQzZ07119r3rx5rnXr1q5BgwbqUFGxcUrl5yZNmrjp06e7/v37e9uUvcMcM2fRX8ZpNINBZBcJ4AxQr14917t3bzd+/HjvBcMbxhEGpIa2atUq8RDyOFiQ+vbt69P3uM6MGTN8xJfiQpSdp6qpEJUcp7ZXDcOM6yQhASwqOU6FqC3jlEgd56tOmDDBFxuaOXOm/z+ic0SHGzduXNZnEXWXUo1TorudOnVykydPdlOmTPEZYKRVk2LNtjxsVLbxCWEs8p8d8iaEEEIIIYQQQtRhtAdYCCGEEEIIIUQmkAAWQgghhBBCCJEJJICFEEIIIYQQQmQCCWAhhBBCCCGEEJlAAlgIIYQQQgghRCaQABZCCCGEEEIIkQkkgIUQQgghhBBCZAIJYCGEEEIIIYQQmUACWAghhBBCCCFEJpAAFkIIIYQQQgiRCSSAhRBCCCGEEEJkAglgIUSd4eijj/avN954oyTXO+uss/z1nnrqqZJcT9QsQ4YM8f337LPPLvAzxgg/o4+zxsUXX+yf/e677y7J9a699lp/Pf4UdQ9bB+PW1Weeecb/7JprrqnIvQkhRCEsXtCnhBC1Ggzfe++9d4H/X2KJJdzyyy/vNtxwQ7f77ru7pk2bukUWWaQi9ygKByEyZcoUt9pqq2VWlHzwwQfeOF933XX9OBainPz3339u8uTJ3ln26aefur/++sutuuqqbvvtt3eHHnqoW3HFFYu6/j///OMeffRRP6a/+OILN2/ePLf66qu7nXfe2R1yyCFu6aWXjv3c3Llz3Ysvvuhef/1199FHH7lvv/3W/fvvv+5///uf22ijjdxee+3ldtppp4Lvi7k1duxY9/TTT7uDDz7YX7OmmT17tpsxY4Zbdtll/T0UCn03bNgw//ehQ4f69o3j1VdfdVdeeaX7888/3TrrrON69uzpVl55ZVcuPv/8c/fOO++4Dz/80Pfhxx9/7McXpHFiPf/8827SpEn+Gr///rsfk40aNXItWrRwa665ZkXHtxA1jQSwEBlnhRVWqPr7b7/95r7//nv/eumll7yIOu+887wwrg2stdZa/s8ll1yyJNdbY401/LMvs8wyJbmeqDluvfVWb7QdddRRcuJEwHBlriCCSsFKK63kr8efWeTvv/92AwYMcDNnzvT/Xmyxxfy68eWXX7oJEya4qVOnugsvvLBgcfjLL7+4Sy+91AsX4NqLLrqoFyK8WKeJ6uPwitK2bVsveA0+y/3ZOo843m677Vznzp0LWje5jyOPPNJdffXVbvTo0a53796uEgIYhy7PX4wAzgeySRDHOCQaNGjgzj//fO80Lic33HCDe/PNNwv+POvgddddV5XJhFN7qaWWct98840XxNOmTXOdOnXyYrYS41uISiABLETG4cvVIKqAt3nUqFHutddec6+88oq788473QknnOBqA4MHDy7p9Xr16lXS64ma4d1333VvvfWWj0oUE92qq5x99tklvd6xxx7rX1mF9RJxgDA46aST3H777ecWX3xxn4WAWGJN7devn1+fCnGmkV6M+CXK265dO9ekSRMvPFmjyfBAyPTv39+LFP4/BPGLUCPSS7QPpx58/fXXbty4ce7JJ5/06/yIESNchw4dCnp+7mfkyJE+RZpn3njjjV1dBLHI9yWCcuutt3Zdu3b1QrLcMK6INJOZxQvHxUMPPZT35x944IEq8duyZUsf8eW+ySRAGBNdHjRokI9qx0W9yz2+hagE2gMshPj/BWHRRX3KaLdu3apSovjSDyMIQizsTJw40f+5yy67LCAIhCgliAjWSGjVqpU78MADvTgAhGCPHj1cvXr13I8//uiFSFpIXUagWjQ3HNPbbLON69Kli//7J598ElurACfe5Zdf7po1a1YlfgGhc8YZZ3gxA0QBSY8uBIQR9xXOvbrGfffd550EiF+cavRrTYhfILp61VVXeQdF8+bN3XrrrZcqewBHB9DX7Ne2+yZro3v37t5RSDr3XXfdVePjW4hKoQiwEGIB+ELDq8+XPnuF8PDypUvUwKJHeH6JGN9///0+EvHDDz/4FMhwzyk/Z88a+8PYv8SXMR5ivNhEJHbdddec6amfffaZ3/dGZOG7777zxgd7rfg894chEgocvtyBNLwtt9xyvmvxu0nXevnll91XX33l9zAtt9xyPgV8k0028XvZ8OpHi78QXTnzzDP9/Ubh+TA6MR4xQGkr0uE23XRTd8ABByxwDwbpiqS04Y0nRfeJJ57wkRjamWfECcHn99hjjxofnRgyDz74oDe6eXYgtZD0OIyvpL1ehbQv76F/2ZuGofXHH3/48UFqLsbVDjvs4Ps5DaTxP/fcc/7vu+22W8HtEPbREUcc4SMujOM5c+Z4A5B0P9qD9NE4wrHDMzBP2KfI//Gc0T2Gb7/9tnv88cf9nz/99JP/HRio7PHE6MxlbLPP87HHHvNtT1oi7Uo/1a9f388R2iGMzITPZnPGwNnFXj+elfRa2pPII+N6gw028FHEffbZJ3bP+Z577plYVIw5zD0SbeJ+ueb666/vaw3wuThHhdUq2GKLLfw9Iwbph/fff9+3Ie2H8GIPImtWJWDusw7QP/RTFEQn92jrROvWrVNdn3YNrxOFucU6Q/vy3mjfbLXVVjmvz/tN4LBGkx5fCIwx+pf1/uSTT04tDhFgL7zwgt9fyx5Xopy2nrIW7L///rFzLRy/zK3oeI4b42m5/fbb/fyFvffe20fha9KxVszvYs2hHeHwww9f4Oes0bTtPffc49fh008/fb6+K/f4FqJSSAALIWJZZZVVqv5uX6DRNFM84hii7B0jChAVRAMHDvSpqAZGOMYvgpkX+6nYe2Ye5RDE9x133OEFIbDniN+DuMLI57M333yzL3xSHYjniy66qCrCgei2e0FsIF4RYFGBlguEAc9nlVExUjDqEZDTp0/3L4rT5Eofx7DgGuzDo/0w4mnP9957z7941mKNtzQgirifX3/91f/b9gTiiOCFUCdisNlmmxXdvowpolMYu+FnaFc+hzOAsZNWAPMMCEDuvRR70tjrx/5L7oU+whCkfRBjvKozsHkW2owxyziP7rNkDJBi+Mgjj1T9H78DQUCKIS+MS6JAcXs8SU0k9dD6jHukHREQCALmWb6p4NxL3759/WcMrsW9MJ95BpwLUZFVHTwfzpGwn7nfWbNm+ReGM+mkSYWcgOgSQsTuiX5hjGC40+eMv0pE+62tNt9880TRt+222/o+pD+YB1arIM31cTwkOQu5PusQzhPGfhpnQPhe+r9QEKms0YwV5kqSYygJxpUVoOI5GQuMZRyrrI+8cDideOKJ830OBxvPzHrC56L72ouJ0tIeN954Y5WDIO73L+zY+CGFOm79APqKeUQ7MoYYTzU1voWoFBLAQohYLPpnXuIoiF8ilaeeemrVni++/MxwuOKKK7whRNSI1CkiERj/CDw8zbfddps3aviTiEEIkbAxY8b4vxMFRGBwHcDAIopExDTfCtV8uSPOMABI+yNigrHMfSLewmhnvrB3CqMTUYPIRRTwfAhghDtRNCKpeMhJP4yDiAkCnyghXnSMUe4Ho4siZFRXJUJGJK/c0D4mfjGWSLc0oUs/Dh8+3Pcv+wzp27DqaSHt+/DDD3vxy9giokKEGQOaz9CGCKPQeZIv9hnEbykEEWORIjBERohU0kc8K0W2cHIQoeR3MU7joG0wHCkmxzMyXmgTm1NEORG/GPKIabIi+BkCj3HO72H/J22OOA2fif+nP7g/5uLxxx/vnQz8DtqR4kBEcnMJyxDei8FLP5x22ml+THLvjNGff/7Z3w/vSQMRfhO/loKJIGcdwKHC/KevGV8dO3aMvQbjhH4l0osIQeTgKCEaTPszDzHA0wrzUoBjCGj/JMKUVaLq+QoEnCfMheg1otjvpp+4nzSOn/BoozSptVEsK4IxgkMirQDGkYnDcMcdd/QZPuYoQgAzTlgL6W+yAcK5xp5cq95M9LpUVe/JhKCwl2WTENkkE6S2wXirbnyGP2P8hAK4nONbiEoiASyEWACMS6IygDEeJ8BITSPqEnqF7YsPIxkjaO211/api2H6Je9HSPCFSgVNBAapWVaNmkiTRXowwM8999z5hC6GEXvfeOULRhkcc8wx80UhEROItiSBmgTRWUQ84ACwfXSAcd++fXvfhryHfVWkT8dFZRCbREHDNEUi70TFSTXH+MMAqwnDa/z48f5+MES5pzDVGe8/fc190T+8F4FUTPuSQQAYvaT5hp9BXJP+XUgKOOmxQHptKaAfEfWhuMLQRqz16dPHCzMcHkkCmKgK78Ooj2ZXsKWATAfGBhFec/KYoMCRwPyh3RG7OIzCSC4ZEIhf5idR6nCe0Y4IkjRiyPqE+Rk+L/OP+cnvTlNUjGe3Y1oQ9jhVwnWAir3cJ89BRgfFeeLul3EZjbTzrPyb7ALSPEm9rWkBTNTRsmNyHYMT/ow5nS/he8txfdqV8WdzvFjhwvhlLbBxlAaEL68obKuh75kjOEtwFiXNtVJCVgVzjrHfpk0bnyacD7a9oBDKcWydjYdc44fvVNZ9xgOZIzU1voWoJKoOIoSowlI7L7nkkqovsoMOOig2ksYe1aSUKKKzgNGQVBUSQ5dII5GuMApBVI0vXas4WYpziC1N2qIpxYKxbkImyegm6g2W8h0He4Xj9ugRgSPlESxFuJwQObJIB30Wt8+XZzUj0J6/mPa1cVFqg8kMuFId8cNzs+8v6fgXi3ogxOIgmhKK3xAiV0RqeU8ofkOI3powsGNIgHRk0hXN8VCK6qt2jVLNE8Y9DhNgr3scrCN2fFJSdJn5gDiOw9omqf3LSbg1JNcRQqHzK247ST7Xz5XWHP7ufK/PuKO6NPOP9sWRVyx2HFA5RJAd0YO4LiZVO1/syCki2fmKX7C6B4W8SrVmhdh4qO6IKxtfZGZEP1uu8S1EJVEEWIiMk2v/Ium3SdHH6D5QA+OECKmlfxItTMKM4zA91qIHCORSnSuK8cR1iSyzb5CII8VjChUNFIsBS/WNA3GPZxxBxvvjohYNGzas1qtubVROiETa78kVWedn7MVE1PMZK+JUSPs2btzYR+1IAye9lmg/Y6pYI5BrJaXtFwJ9nOSE4X5x1JAuyV7duBRSnBxJWOQcYUuKdRJmlIZVeu2zjL8wZbEY6EeK/RBppnIwEXhSTnNFf3JBm5gTISm6yP3Txohfe3/cXEpyttkaURPzpC5xyy23+KJpQDZHKTImbM7ZHEwLjhcygpgPOHjIvoiKXduPXg6xGJ23zDHaiG0I+e79ZauDEGLhRwJYiIxjqcdAJAAvPhErqnrmqiCaZIBgnJCWCVaYpzowagyLPiUV7CgEokdEUolysp+MF6IGwxrxsO+++6ZK/6OwE1QnDDD8EcD2/ii5CrSYsK6JI6jC+8v1TGFhND5jAriQ9mV8ka5MSiMRZYsqc/wWQpvIeiFFrGzsxRVWK4Rc7UHkA6Oftkjq43B+RbFIGeM/nANp5gnztVTHsSDojzvuOH/2N9V4eVm/k9qOIK6uqnCh8ySXcMq1h9mK76WdJ5Z2Xd3Z6LkI7ytX/5EKHveZNNcPrxEl/N35XB9Bx95sIMumVKnjFgm0OZgGHGjscQ+/MxjXFnlECON4g3zmSrFQlwGHHun17D3m90drVdQGGA98J1fXZja+wrWk3ONbiEoiASxExsnX2IuSFPkMPfYXXHBB6uhUKVKeoyCGOnXq5PcaY9CQOkqUmtRVXhTpwfBnP6qoufbFoORoDYQzn8EIpvI1LyJBpN+nNToRhAjLfJ0v5SZXIS6bKxR3on0qPU/MmYFzgj5hLyN9QtEu0rV5UZX7nHPOKZmDoVIQXUxyWuQLxj4v0j7DvZNRwp+lyWoJ31uq67OPFkEHFO9jH3apsCh82uwLKzjFnGUrACn9OGNCMcWawLgDOxmgnOBYYU3jvtiWQ9E+qG0imPFAv+QaP4hbWy9DZ1W5x7cQlaR2f4MJIRY6MH4sLTRtZeUwYlbIZ6sD48r2WnJ/GPhUkaWQEYYhUa6kvZjRe6QiMsIgF/bzXFHAhYHw/rjnpGh4+Lxxz1RI+xLxtfMpEYREhUnD5UxQjE4ijmmK3pCZgAAuVUpsLsOPSJf9nkL6mL3WjKNCxrrt0yYqRop0qaLAZgQjjEwcsb+WvmBvP2IAcYJzojqsTXK1YTiuyp3WGsJ5xUlnFqeBLAdz9iQR7k/OVU03zplDPxPtz7XH2X63ZV0kMXr0aF+ZHqgYXmqHn82FtP2Ik4U5gLOoR48esRkDpdqXnga+x6wIIw4h5gBrVK790lRrt+0JaaG4HlHwUsJ4M0dkEuHPouOnnONbiEqiIlhCiJJCZKhBgwb+7xzlkxbbM8m+2XJWlMS4QZBRiZrUb6IKFADLB0vNpXhXUkEW9sKa4W/HRC2skMpsUZtcbWA/wzC39OdSti8GMHuHu3Tp4o1BSCoglgSVx2HOnDmuFCDikyJOCHtLvS2kj22s0y65UlxzfZbxZ6nK5YK9zVTCtt+Zb59YmyBw7Yi0KNy/FcFb2OdJHLZnngyGpDRR6x+2daSttGzXZ19s0ji06+OYSCqWRdpzKH6TiooVA3UBwjmYL6EDJCldPtfaYdkQ5YgMmwimRgGQOn7TTTfldALYloi0r0L3Tuczfvg+CmsIxI0fxk60tke5x7cQlUICWAhRctjzCZz/aoVWkohG6po2berTrhAWo0aNKolRk2tPGoLd0lTzTSvlSBdA4FrF6ygcf2RiMTwaaGGE5zYDb9KkSbHRFp6Vn4XPX0z75voM77cU27SpvhRtgqSCSmnBaJwyZUqscLMCb0RJCjlDlerSGNhEce24oCSolh5WaCVyztE1wDFMpPQWS3V7N01c5Xu+MsazVQamIF4cEydOrHJ0kXpd26BQIO1Bmqjtq42KQttrzHvTwpFU5tCxSu0hROfMgWDvjRO/YdpzOcRveASZzcF8sWJ5iMC4tQeBTK2AJCxVulzbHuhf0q9tfFK4j7Pa476bOAaJuVzIq9RHIAHHltE+3KsdeRVCmzEHgeKF0UySco9vISqFBLAQouRQLAfRx5cuKWFjx46dLw0SQ37WrFneiOC826gxRIQC+GIdOHCgmz17dtXP8UIjqgcMGJC30U9BkzFjxvhUu9DIZ1/ZkCFD/DURWvnuVybCbWfXjhw50hsG5h3HgLv++ut9qqgdh5TrCJNygkgjqpDrZcdWkIbMcUY4JDhTNkzjw/vP/2EsESk+7LDDim5f9ofTdhjvobBjnPD/fNaOIUmDGd+kVJYibZLxyD55xL9FaRHF7A004dG6deuCro2ItaOUqK49dOjQ+dIJcQIx9kkj79Chw3zzAE455RQfXadiLuc0E4lBKIfp5CNGjMg7YstcGzZsmHdchWKCMcEcZs6GR9JUB+Pejj+i4jf3Yn3CmCCllGrEgAOmkKJnlYaIl50DjtOLvevWB2Sx9OvXz48bUpnjhCdjiEr8vNhjHYV11OYA7YcItqwToqKsr4ADhvPGc+35pZJxueoc0K8WYUwrgIk6UuyK74tBgwZVZQtYdgOiMpcjzJxPrGVJhc2KBRHId5WJPPqZdaEm9iOzpoZrdrheRtfzaEYS67Wd5IDQZS2xz9PO/fv39w4o2t+O7ivl+BZiYUV7gIUQZTEWSGNF/CBW+eLkhSeanyFczXCwKq4hnLuI0U01Wo5k4YUxzQvD3D6br/FBZAHvNy8MKUQNX9om1vg/jMNc++eitG/f3kfuSJFFsBGtxnsePhvGZrNmzVylIHLSpk2bnO9hf223bt18Jd6uXbt6xwL7vRBUVoHVxD0CmfdE0xQLaV/aCccBL/sMhlWYZsce1LRF1PgdHOlCVWrGjRlvhUL/4QBAfJD6SB+H4hDjkihLoSCAEbrjxo1zU6dO9S/GOW3P78l15in7qek7RAN9xtFFzCfakva19Ox825A+s2JXYWQtPNuTIlhpqgZT5IzoJYXQcCJQIZz7wwi3++MYpHbt2rnaCpWUeUbSlHHqUWGaPrR2IwrO3tZCj13D+YEDirNp6WucHqyjNldIPe3evfsCkXkEKY4VYI6xt55XEqxXhYoY5pqNyTTrKNAuRKZpO7YVdOzY0c8zxgdrCO3HesvalORIwlGAQ2Dw4MFu+PDhVVs62KteqkJftC/7xmlL5injmbW+bdu2ZStKZ84jHFNxRNd3nGjR7Sn0KSnQzGsizYhg+64C1hoKfiVtayn3+BaiEkgACyHKAl+GfCkSTSKFlOggQgmDAQGFkYThS8pzHEQkOSuW1DeiJEQGEUj169f3xzRZqnQ+9OzZ00eviGpiFFoUCsOJ6ANGetroE8/Xq1cvb1RMmzbNR+cw6vGEs4+Va/J8tQkiNxjY7BWk34iiYtixp4+oHwayFV8qtn3ZV0dkEoOXNDo+g8GLMc/5yAjXNEfuRB0oGGqcLVusACYVm36mTbge90rfs18VwzrfaGgStC+RF8Yz0RXGOo4LjFMcDkRg2HuLyGZcRWnUqJF3NBFNpc+InDMOmWN8lkyFfNuR4j5cA6cO16FPEMVUduV5SbG1zIc0YEAzl0kdZYzgOMIARyyRLcJ1802rXhhBkJLRwHYI1jqcEbQbaxXjgyrfcfMmXxBzl112mV8LEUNE/HGMUHCI/mjevHms+AidJ6y71VW9DiOLaWENhELnG44m9v0zz9i+wFrAGCb6TcaJRR2T6Ny5sxd2OFxZg6ywXKnTohmnZLzwJ2s/Dh3aFgdOOUVwMXBf3DNjEdGOI4W+Zq1lmwLjk7W6UuNbiEqwyH81kb8hhBBC1BBEJijahJFHRCTuTGmEZp8+ffzP4vbekXaJEGzZsqVPTxUiqxD1RFD27t071qmHU4goNU4Ntn/oLFghxMJO7XW5CiGEEDFggBM1wr+bK+VTCFE8zDHmGlk7Er9CiNqABLAQQog6B+nJ7GsmbS/p+A8hRHEwtyZPnuzTl/M5G1oIIRYGtAdYCCFEnYMiLVRtJdUZI93OFRZClA7mFtkW7DOvVLV7IYRIiwSwEEKIOgn7FWtbITIhahMUueMlhBC1CQlgIYQQIqYIlhBCCCHqHqoCLYQQQgghhBAiE6gIlhBCCCGEEEKITCABLIQQQgghhBAiE0gACyGEEEIIIYTIBBLAQgghhBBCCCEygQSwEEIIIYQQQohMIAEshBBCCCGEECITSAALIYQQQgghhMgEEsBCCCGEEEIIITKBBLAQQgghhBBCiEwgASyEEEIIIYQQIhNIAAshhBBCCCGEyAQSwEIIIYQQQgghMoEEsBBCCCGEEEKITCABLIQQQgghhBAiE0gACyGEEEIIIYTIBBLAQgghhBBCCCEygQSwEEIIIYQQQohMIAEshBBCCCGEECITSAALIYQQQgghhMgEEsBCCCGEEEIIITKBBLAQQgghhBBCiEwgASyEEEIIIYQQIhNIAAshhBBCCCGEyAQSwEIIIYQQQgghMoEEsBBCCCGEEEIIlwX+D2A9Cs0xqX4yAAAAAElFTkSuQmCC", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "============================================================\n", - "PARETO-OPTIMAL SOLUTIONS\n", - "============================================================\n", - "\n", - "Trial 30 (Solution #1):\n", - " Precision: 0.0300 (target: 0.0200, loss: 0.0100)\n", - " Feature stability loss: 1.7814\n", - " Parameters:\n", - " temporal.mean_amount_sar: 850\n", - " temporal.std_amount_sar: 328\n", - " temporal.mean_outcome_sar: 494\n", - " temporal.std_outcome_sar: 151\n", - " temporal.prob_spend_cash: 0.3145\n", - " temporal.mean_phone_change_frequency_sar: 1432\n", - " temporal.std_phone_change_frequency_sar: 388\n", - " temporal.mean_bank_change_frequency_sar: 1326\n", - " temporal.std_bank_change_frequency_sar: 405\n", - " temporal.prob_participate_in_multiple_sars: 0.2140\n", - " temporal.mean_duration_alert: 13.5156\n", - " temporal.std_duration_alert: 13.1792\n", - " temporal.burstiness_bias_alert: -2.3909\n", - " ml_selector.structure_weights.degree: 0.3531\n", - " ml_selector.structure_weights.betweenness: 0.1479\n", - " ml_selector.structure_weights.pagerank: 0.3997\n", - " ml_selector.kyc_weights.init_balance: 0.1254\n", - " ml_selector.kyc_weights.salary: 0.0918\n", - " ml_selector.kyc_weights.age: 0.0996\n", - " ml_selector.participation_decay: 0.7622\n", - "\n", - "Trial 63 (Solution #2):\n", - " Precision: 0.2100 (target: 0.0200, loss: 0.1900)\n", - " Feature stability loss: 1.2504\n", - " Parameters:\n", - " temporal.mean_amount_sar: 742\n", - " temporal.std_amount_sar: 396\n", - " temporal.mean_outcome_sar: 516\n", - " temporal.std_outcome_sar: 139\n", - " temporal.prob_spend_cash: 0.1530\n", - " temporal.mean_phone_change_frequency_sar: 1331\n", - " temporal.std_phone_change_frequency_sar: 383\n", - " temporal.mean_bank_change_frequency_sar: 1298\n", - " temporal.std_bank_change_frequency_sar: 276\n", - " temporal.prob_participate_in_multiple_sars: 0.1721\n", - " temporal.mean_duration_alert: 25.2527\n", - " temporal.std_duration_alert: 9.6108\n", - " temporal.burstiness_bias_alert: 1.1035\n", - " ml_selector.structure_weights.degree: 0.1302\n", - " ml_selector.structure_weights.betweenness: 0.1595\n", - " ml_selector.structure_weights.pagerank: 0.3844\n", - " ml_selector.kyc_weights.init_balance: 0.1083\n", - " ml_selector.kyc_weights.salary: 0.0990\n", - " ml_selector.kyc_weights.age: 0.0437\n", - " ml_selector.participation_decay: 0.6318\n", - "\n", - "Trial 64 (Solution #3):\n", - " Precision: 0.1500 (target: 0.0200, loss: 0.1300)\n", - " Feature stability loss: 1.2665\n", - " Parameters:\n", - " temporal.mean_amount_sar: 795\n", - " temporal.std_amount_sar: 392\n", - " temporal.mean_outcome_sar: 510\n", - " temporal.std_outcome_sar: 109\n", - " temporal.prob_spend_cash: 0.1242\n", - " temporal.mean_phone_change_frequency_sar: 1261\n", - " temporal.std_phone_change_frequency_sar: 359\n", - " temporal.mean_bank_change_frequency_sar: 1306\n", - " temporal.std_bank_change_frequency_sar: 278\n", - " temporal.prob_participate_in_multiple_sars: 0.1832\n", - " temporal.mean_duration_alert: 25.6783\n", - " temporal.std_duration_alert: 12.4860\n", - " temporal.burstiness_bias_alert: 1.9265\n", - " ml_selector.structure_weights.degree: 0.1605\n", - " ml_selector.structure_weights.betweenness: 0.2590\n", - " ml_selector.structure_weights.pagerank: 0.4893\n", - " ml_selector.kyc_weights.init_balance: 0.1097\n", - " ml_selector.kyc_weights.salary: 0.0873\n", - " ml_selector.kyc_weights.age: 0.0386\n", - " ml_selector.participation_decay: 0.6833\n", - "\n", - "Trial 72 (Solution #4):\n", - " Precision: 0.0400 (target: 0.0200, loss: 0.0200)\n", - " Feature stability loss: 1.6835\n", - " Parameters:\n", - " temporal.mean_amount_sar: 841\n", - " temporal.std_amount_sar: 385\n", - " temporal.mean_outcome_sar: 475\n", - " temporal.std_outcome_sar: 113\n", - " temporal.prob_spend_cash: 0.1237\n", - " temporal.mean_phone_change_frequency_sar: 1276\n", - " temporal.std_phone_change_frequency_sar: 401\n", - " temporal.mean_bank_change_frequency_sar: 1277\n", - " temporal.std_bank_change_frequency_sar: 290\n", - " temporal.prob_participate_in_multiple_sars: 0.2220\n", - " temporal.mean_duration_alert: 22.8719\n", - " temporal.std_duration_alert: 16.0229\n", - " temporal.burstiness_bias_alert: 2.4977\n", - " ml_selector.structure_weights.degree: 0.2538\n", - " ml_selector.structure_weights.betweenness: 0.2191\n", - " ml_selector.structure_weights.pagerank: 0.4896\n", - " ml_selector.kyc_weights.init_balance: 0.1290\n", - " ml_selector.kyc_weights.salary: 0.0667\n", - " ml_selector.kyc_weights.age: 0.0206\n", - " ml_selector.participation_decay: 0.6965\n", - "\n", - "Trial 80 (Solution #5):\n", - " Precision: 0.0700 (target: 0.0200, loss: 0.0500)\n", - " Feature stability loss: 1.3648\n", - " Parameters:\n", - " temporal.mean_amount_sar: 783\n", - " temporal.std_amount_sar: 391\n", - " temporal.mean_outcome_sar: 471\n", - " temporal.std_outcome_sar: 121\n", - " temporal.prob_spend_cash: 0.1471\n", - " temporal.mean_phone_change_frequency_sar: 1264\n", - " temporal.std_phone_change_frequency_sar: 439\n", - " temporal.mean_bank_change_frequency_sar: 1286\n", - " temporal.std_bank_change_frequency_sar: 318\n", - " temporal.prob_participate_in_multiple_sars: 0.1690\n", - " temporal.mean_duration_alert: 22.4228\n", - " temporal.std_duration_alert: 12.1043\n", - " temporal.burstiness_bias_alert: 0.2326\n", - " ml_selector.structure_weights.degree: 0.2241\n", - " ml_selector.structure_weights.betweenness: 0.1629\n", - " ml_selector.structure_weights.pagerank: 0.3896\n", - " ml_selector.kyc_weights.init_balance: 0.1283\n", - " ml_selector.kyc_weights.salary: 0.0703\n", - " ml_selector.kyc_weights.age: 0.0388\n", - " ml_selector.participation_decay: 0.7412\n", - "\n", - "Trial 82 (Solution #6):\n", - " Precision: 0.0600 (target: 0.0200, loss: 0.0400)\n", - " Feature stability loss: 1.5154\n", - " Parameters:\n", - " temporal.mean_amount_sar: 730\n", - " temporal.std_amount_sar: 372\n", - " temporal.mean_outcome_sar: 494\n", - " temporal.std_outcome_sar: 134\n", - " temporal.prob_spend_cash: 0.1030\n", - " temporal.mean_phone_change_frequency_sar: 1295\n", - " temporal.std_phone_change_frequency_sar: 423\n", - " temporal.mean_bank_change_frequency_sar: 1294\n", - " temporal.std_bank_change_frequency_sar: 423\n", - " temporal.prob_participate_in_multiple_sars: 0.1224\n", - " temporal.mean_duration_alert: 24.8388\n", - " temporal.std_duration_alert: 8.5240\n", - " temporal.burstiness_bias_alert: -1.6502\n", - " ml_selector.structure_weights.degree: 0.1537\n", - " ml_selector.structure_weights.betweenness: 0.2201\n", - " ml_selector.structure_weights.pagerank: 0.4779\n", - " ml_selector.kyc_weights.init_balance: 0.0562\n", - " ml_selector.kyc_weights.salary: 0.0742\n", - " ml_selector.kyc_weights.age: 0.0631\n", - " ml_selector.participation_decay: 0.8062\n", - "\n", - "Trial 95 (Solution #7):\n", - " Precision: 0.3200 (target: 0.0200, loss: 0.3000)\n", - " Feature stability loss: 1.2082\n", - " Parameters:\n", - " temporal.mean_amount_sar: 798\n", - " temporal.std_amount_sar: 386\n", - " temporal.mean_outcome_sar: 492\n", - " temporal.std_outcome_sar: 119\n", - " temporal.prob_spend_cash: 0.1333\n", - " temporal.mean_phone_change_frequency_sar: 1277\n", - " temporal.std_phone_change_frequency_sar: 377\n", - " temporal.mean_bank_change_frequency_sar: 1273\n", - " temporal.std_bank_change_frequency_sar: 326\n", - " temporal.prob_participate_in_multiple_sars: 0.2158\n", - " temporal.mean_duration_alert: 18.7926\n", - " temporal.std_duration_alert: 12.0775\n", - " temporal.burstiness_bias_alert: -0.2582\n", - " ml_selector.structure_weights.degree: 0.2535\n", - " ml_selector.structure_weights.betweenness: 0.1576\n", - " ml_selector.structure_weights.pagerank: 0.3451\n", - " ml_selector.kyc_weights.init_balance: 0.1221\n", - " ml_selector.kyc_weights.salary: 0.0932\n", - " ml_selector.kyc_weights.age: 0.0234\n", - " ml_selector.participation_decay: 0.6584\n", - "\n", - "============================================================\n", - "RECOMMENDATION\n", - "============================================================\n", - "\n", - "To select a trial:\n", - " 1. Low precision loss = better match to target\n", - " 2. Low importance loss = more stable features\n", - " 3. Balance both for general use\n", - "\n", - "Trial 30 has the best balance\n", - "Run next cell to apply it (or choose a different trial number)\n" - ] - } - ], + "outputs": [], "source": [ "# Display Pareto front\n", "from IPython.display import Image, display\n", @@ -1015,44 +506,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "APPLYING OPTIMIZED PARAMETERS\n", - "============================================================\n", - "\n", - "Regenerating data with optimized parameters...\n", - "This may take a few minutes...\n", - "\n", - "\n", - "✓ Optimized transaction data generated: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/temporal/tx_log.parquet\n", - "\n", - "Generated 616,797 transactions\n", - "Banks: ['source', 'bank_a']\n", - "SAR transactions: 1,460 (0.24%)\n", - "Time range: steps 0 to 99\n", - "\n", - "============================================================\n", - "COMPARISON: Original vs Optimized\n", - "============================================================\n", - " Total transactions: 604,436 → 616,797\n", - " SAR rate: 0.24% → 0.24%\n", - "\n", - "✅ Data optimization complete!\n", - "The optimized data should achieve ~2.0% precision in top 100 alerts.\n", - "\n", - "Proceed to Step 3 to preprocess this optimized data.\n" - ] - } - ], + "outputs": [], "source": [ "# ===== SELECT WHICH TRIAL TO USE =====\n", - "SELECTED_TRIAL = best_trials[4].number # Default: use first (best balance)\n", + "SELECTED_TRIAL = best_trials[0].number # Default: use first (best balance)\n", "# You can change this to any trial number from the list above\n", "# ======================================\n", "\n", @@ -1144,287 +603,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Preprocessing configuration:\n", - " Raw data: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/temporal/tx_log.parquet\n", - " Output dir: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/preprocessed\n", - " Split by pattern: True\n", - "\n", - "Preprocessing transactions...\n", - "Generating temporal features with rolling windows...\n", - "\n", - "\n", - "============================================================\n", - "PATTERN DISTRIBUTION ACROSS SPLITS\n", - "============================================================\n", - "\n", - "Unique SAR patterns per split:\n", - " Train: 28 patterns - IDs: [22737, 22755, 22758, 22759, 22764, 22765, 22766, 22767, 22771, 22773, 22774, 22775, 22777, 22778, 22779, 22780, 22781, 22782, 22784, 22785, 22786, 22787, 22788, 22789, 22790, 22791, 22792, 22793]\n", - " Val: 20 patterns - IDs: [22744, 22749, 22759, 22761, 22768, 22769, 22774, 22779, 22780, 22781, 22782, 22783, 22785, 22786, 22787, 22788, 22789, 22790, 22791, 22792]\n", - " Test: 15 patterns - IDs: [22729, 22756, 22757, 22760, 22770, 22775, 22776, 22777, 22781, 22784, 22785, 22788, 22790, 22791, 22793]\n", - "\n", - "⚠️ PATTERN OVERLAP DETECTED (potential data leakage):\n", - " Train ∩ Val: 14 patterns - IDs: [22759, 22774, 22779, 22780, 22781, 22782, 22785, 22786, 22787, 22788, 22789, 22790, 22791, 22792]\n", - " Train ∩ Test: 9 patterns - IDs: [22775, 22777, 22781, 22784, 22785, 22788, 22790, 22791, 22793]\n", - " Val ∩ Test: 5 patterns - IDs: [22781, 22785, 22788, 22790, 22791]\n", - "\n", - " Tip: Enable 'split_by_pattern: true' in preprocessing.yaml\n", - " to prevent the same pattern appearing in multiple splits.\n", - "============================================================\n", - " Saved centralized/trainset_nodes.parquet: 9,888 samples\n", - " Saved centralized/trainset_edges.parquet: 24,409 samples\n", - " Saved centralized/valset_nodes.parquet: 9,888 samples\n", - " Saved centralized/valset_edges.parquet: 24,409 samples\n", - " Saved centralized/testset_nodes.parquet: 9,888 samples\n", - " Saved centralized/testset_edges.parquet: 24,409 samples\n", - "\n", - "Splitting data for 1 banks: ['bank_a']\n", - " ✓ Saved data for bank: bank_a\n", - "\n", - "Generating dataset summary...\n", - "\n", - "============================================================\n", - "FEATURE ANALYSIS\n", - "============================================================\n", - "WARNING - matplotlib.text - posx and posy should be finite values\n", - "WARNING - matplotlib.text - posx and posy should be finite values\n", - "WARNING - matplotlib.text - posx and posy should be finite values\n", - "WARNING - matplotlib.text - posx and posy should be finite values\n", - "WARNING - matplotlib.text - posx and posy should be finite values\n", - "WARNING - matplotlib.text - posx and posy should be finite values\n", - "\n", - "Feature distributions (SAR vs non-SAR) - 9 groups:\n", - "\n", - "Balance:\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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- "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 800 - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "City distribution analysis:\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 900 - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "✅ Preprocessing complete!\n" - ] - } - ], + "outputs": [], "source": [ "from src.feature_engineering import DataPreprocessor, summarize_dataset\n", "from src.utils.config import load_preprocessing_config\n", @@ -1628,41 +809,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "CENTRALIZED TRAINING - GraphSAGE\n", - "============================================================\n", - "\n", - "Training GraphSAGE on centralized data:\n", - " Device: cpu\n", - " Nodes: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/preprocessed/centralized/trainset_nodes.parquet\n", - " Edges: /Users/jostman/Projects/AMLGentex/experiments/tutorial_demo/preprocessed/centralized/trainset_edges.parquet\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for /: 'str' and 'str'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 47\u001b[39m\n\u001b[32m 45\u001b[39m results_dir = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mexperiment_root / \u001b[39m\u001b[33m'\u001b[39m\u001b[33mresults\u001b[39m\u001b[33m'\u001b[39m\u001b[33m / \u001b[39m\u001b[33m'\u001b[39m\u001b[33mcentralized\u001b[39m\u001b[33m'\u001b[39m\u001b[33m / \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 46\u001b[39m os.makedirs(results_dir, exist_ok=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m---> \u001b[39m\u001b[32m47\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[43mresults_dir\u001b[49m\u001b[43m \u001b[49m\u001b[43m/\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mresults.pkl\u001b[39;49m\u001b[33;43m'\u001b[39;49m, \u001b[33m'\u001b[39m\u001b[33mwb\u001b[39m\u001b[33m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m 48\u001b[39m pickle.dump(results_centralized, f)\n\u001b[32m 50\u001b[39m \u001b[38;5;66;03m# Display final metrics\u001b[39;00m\n", - "\u001b[31mTypeError\u001b[39m: unsupported operand type(s) for /: 'str' and 'str'" - ] - } - ], + "outputs": [], "source": [ "from src.ml.training.centralized import centralized\n", "from src.ml import clients, models\n",